TF 2 P-growth: An Efficient Algorithm for Mining Frequent Patterns without any Thresholds

Size: px
Start display at page:

Download "TF 2 P-growth: An Efficient Algorithm for Mining Frequent Patterns without any Thresholds"

Transcription

1 TF 2 P-growth: An Effcent Algorthm for Mnng Frequent Patterns wthout any Thresholds Yu HIRATE, Ego IWAHASHI, and Hayato YAMANA Graduate School of Scence and Engneerng, Waseda Unversty {hrate, ego, yamana}@yama.nfo.waseda.ac.jp Abstract Conventonal frequent pattern mnng algorthms requre some user-specfed mnmum support, and then mne frequent patterns wth support values that are hgher than the mnmum support. As t s dffcult to predct how many frequent patterns wll be mned wth a specfed mnmum support, the Top-k mnng concept has been proposed. The Top-k Mnng concept s based on an algorthm for mnng frequent patterns wthout a mnmum support, but wth the number of most k frequent patterns ordered accordng to ther support values. However, the Top-k mnng concept stll requres a threshold k. Therefore, users must decde the value of k before ntatng mnng. In ths paper, we propose a new mnng algorthm, called TF 2 P-growth, whch does not requre any thresholds. Ths algorthm mnes patterns wth the descendng order of ther support values wthout any thresholds and returns frequent patterns to users sequentally wth short response tme. 1. Introducton Due to recent developments n network nfrastructure and both prce reducton and ncreases n capacty of storage devces, t has become commonplace to archve large amounts of data. It s mportant to analyze such large data sets because they may contan new knowledge. The dscovery of such knowledge requres data mnng technology. Both the long response tme when mnng large amounts of data and usablty wth regard to specfcaton of some threshold values are fundamental problems n data mnng, especally n frequent pattern mnng. Many algorthms have been proposed to resolve these problems. Conventonal frequent pattern mnng algorthms are classfed nto two categores: the canddate-generatonand-test approach and the pattern-growth approach. Canddate-generaton-and-test approach algorthms, such as Apror[1], suffer from both the generaton of huge numbers of canddates and scannng of the dataset many tmes to count the frequency of generated patterns, resultng n long response tmes. Although most frequent pattern mnng algorthms proposed pror to 2000 were based on the canddategeneraton-and-test approach, another approach called pattern-growth has also been proposed. Pattern-growth approach algorthms, such as FP-growth[2], scan the dataset only a few tmes. Moreover, pattern-growth approach algorthms mne frequent patterns wthout generatng any canddates. Thus, n most cases, algorthms based on the pattern-growth approach mne frequent patterns faster than those based on the canddategeneraton-and-test approach. However, the user must stll wat for a long tme to mne large numbers of frequent patterns f users fal to specfy a mnmum support. On the other hand, algorthms based on another concept have also been proposed. Ths s called concept mnng, examples of whch nclude maxmal pattern mnng[][4], closed pattern mnng[5][6], and Top-k mnng[7][8]. Usng maxmal pattern mnng or closed pattern mnng concept algorthms, users can mne frequent patterns contanng only ther superset patterns excludng any subset patterns. Usng Top-k mnng concept algorthms, users can mne the most k-frequent patterns n descendng order of support wthout specfyng a mnmum support. Wth regard to usablty, the Top-k mnng concept s mportant because the user does not have to specfy a mnmum support, whch s usually dffcult to choose when mnng a moderate number of frequent patterns. The Top-k mnng concept algorthms Itemset-Loop/ItemsetLoop and TFP-Mnng were proposed by Fu et al. [7] and Han et al. [8] n 2000 and 2002, respectvely. Itemset-Loop/Itemset-Loop mnes the most k-frequent patterns wth lengths shorter than the user-specfed value of m. In contrast, TFP-Mnng mnes the most k-closed frequent patterns wth lengths longer than the userspecfed value of m. However, such Top-k mnng concept algorthms stll requre a threshold k before the ntaton of mnng. Therefore, users must decde the value of k. When the value of k s too large, mnng takes a long tme. In contrast, usng a value of k that s

2 too small usually results n mnng only useless patterns even though the mnng tme s short. Therefore, there are stll some dffcultes n specfyng the value of k. In ths paper, we propose a new mnng algorthm, called TF 2 P-growth, whch does not requre any threshold values. TF 2 P-growth, based on FP-growth, mnes patterns wth descendng order of support values. Then, t returns frequent patterns to users sequentally wth a short response tme. The remander of ths paper s organzed as follows. The terms used are explaned n secton 2. Related works are descrbed n secton. Then, we ntroduce our proposed method n secton 4. Secton 5 reports the performance evaluaton of our proposed method. In secton 6, we summarze our work and dscuss some future research drectons. 2.Terms defnton Let I = { 1, 2,, n} be a set of tems. An temset X s a non-empty subset of I. An temset wth m tems s called an m-temset. Duple < td, X > s called a transacton where td s a transacton dentfer and X s an temset. A transacton database TDB s a set of transactons. Gven a transacton database TDB, the support of an temset X, denoted as sup(x ), s the number of transactons ncludng the temset X. A frequent pattern s defned as the temset whose support s hgher than the mnmum support mn_sup. When temsets are algned by ther support n descendng order, the support of the k-th temset s denoted as α. Then, the Top-k frequent patterns are defned as the temsets whose support values are hgher than α.. Related Works In ths secton, researches on both basc frequent pattern mnng and concept mnng are descrbed..1. Basc frequent pattern mnng algorthms.1.1. Apror[1]. Apror s a basc breadth frst algorthm. The theory of Apror s based on the fact that the temset X ' contanng temset X s never frequent f temset X s nfrequent. Based on the theory, Apror teratvely generates a set of canddate frequent patterns whose lengths are ( k + 1) from the k-temsets (for k 1). Then, ther correspondng supports are checked. There are many varants that have mproved on Apror by further reducng the number of canddates generated[9], or by reducng the number of TDB scans[10] FP-growth[2]. In 2000, Han et al. proposed the FP-growth algorthm the frst pattern-growth concept algorthm. FP-growth constructs an FP-tree structure and mnes frequent patterns by traversng the constructed FPtree. The FP-tree structure s an extended prefx-tree structure nvolvng crucal condensed nformaton of frequent patterns. a) FP-tree structure The FP-tree structure has suffcent nformaton to mne complete frequent patterns. It conssts of a prefxtree of frequent 1-temset and a frequent-tem header table. Each node n the prefx-tree has three felds: tem-name, count, and node-lnk. tem-name s the name of the tem. count s the number of transactons that consst of the frequent 1-tems on the path from root to ths node. node-lnk s the lnk to the next same temname node n the FP-tree. Each entry n the frequent-tem header table has two felds: tem-name and head of node-lnk. tem-name s the name of the tem. head of node-lnk s the lnk to the frst same tem-name node n the prefx-tree. b) Constructon of FP-tree FP-growth has to scan the TDB twce to construct an FP-tree. The frst scan of TDB retreves a set of frequent tems from the TDB. Then, the retreved frequent tems are ordered by descendng order of ther supports. The ordered lst s called an F-lst. In the second scan, a tree T whose root node R labeled wth null s created. Then, the followng steps are appled to every transacton n the TDB. Here, let a transacton represent [ p P] where p s the frst tem of the transacton and P s the remanng tems. In each transacton, nfrequent tems are dscarded. Then, only the frequent tems are sorted by the same order of F-lst. Call nsert_tree ( p P, to construct an FP-tree. The functon nsert_tree ( p P, appends a transacton [ p P] to the root node R of the tree T. Pseudo code of the functon nsert_tree ( p P, s shown n Fgure 1. An example of an FP-tree s shown n Fgure 2. Ths FP-tree s constructed from the TDB shown n Table 1 wth mn_sup =. In Fgure 2, every node s represented by ( tem name : count). Lnks to next same temname node are represented by dotted arrows.

3 Table 1. Sample TDB TID Items Frequent Items 100 f, a, c, d, g,, m, p f, c, a, m, p 200 a, b, c, f, l, m, o f, c, a, b, m 00 b, f, h, j, o f, b 400 b, c, k, s, p c, b, p 500 a, f, c, e, l, p, m, n f, c, a, m, p functon nsert_tree ( p P, { } let N be a drect chld node of R, such that N s tem-name = p s tem-name. f ( R has a drect chld node N ) { ncrement N s count by 1. } else{ create a new node M lnked under the R. set M s tem-name equal to p. set M s count equal to 1. } call nsert_tree ( P, N). Fgure 1. Pseudo code of nsert_tree ( p P, c) FP-growth FP-growth mnes frequent patterns from an FP-tree. To generate complete frequent patterns, FP-growth traverses all the node-lnks from head of node-lnks n the FPtree s header table. For any frequent tem a, all possble frequent patterns ncludng a can be mned by followng a s node-lnk startng from a s head n the FP-tree header table. In detal, a s prefx path from a s node to root node s extracted at frst. Then, the prefx path s transformed nto a s condtonal pattern base, whch s a lst of tems that occur before a wth the support values of all the tems along the lst. Then, FP-growth constructs a s condtonal FP-tree contanng only the paths n a s condtonal pattern base. It then mnes all the frequent patterns ncludng tem a from a s condtonal FP-tree. For example, we descrbe how to mne all the frequent patterns ncludng tem p from the FP-tree shown n Fgure 2. For node p, FP-growth mnes a frequent pattern (p:) by traversng p s node-lnks through node (p:2) to node (p:1). Then, t extracts p s prefx paths; Header Table Item f c a b m p Head of node-lnks Fgure 2. Example of an FP-tree {(f:2,c:2,a:2,m:2),(c:1,)} p s condtonal pattern base Header Table Item c m:2 p:2 Head of node-lnks Fgure. p s condtonal FP-tree <f:4,c:,a:,m:2> and <c:1,>. To study whch tems appear together wth p, the transformed path <f:2,c:2,a:2,m:2> s extracted from <f:4,c:,a:,m:2> because the support value of p s 2. Smlarly, we have <c:1,>. The set of these paths {(f:2,c:2,a:2,m:2),(c:1,)} s called p s condtonal pattern base. FP-growth then constructs p s condtonal FP-tree contanng only the paths n p s condtonal pattern base as shown n Fgure. As only c s an tem occurrng more than mn_sup appearng n p s condtonal pattern base, p s condtonal FP-tree leads to only one branch (c:). Hence, only one frequent pattern (cp:) s mned. The fnal frequent patterns ncludng tem p are (p:) and (cp:)..2. Concept mnng algorthms root.2.1. Maxmal Pattern Mnng concept. Basc frequent pattern mnng often mnes a huge number of frequent patterns. However, t s dffcult to fnd new knowledge from such huge numbers of frequent patterns. To resolve ths problem, maxmal pattern mnng algorthms, such as Max-Mner[] and FPmax[4], whch mne only the maxmal frequent patterns, were proposed. c: root f:4 c:1 c: a: m:1 p:1

4 Defnton1 (Maxmal frequent pattern) A pattern X s defned as a maxmal frequent pattern ff the followng two condtons are satsfed smultaneously: (1) The support value of X s hgher than mn_sup. (2) There exsts no pattern X whose support value s hgher than mn_sup, where X s any superset of X Closed Pattern Mnng concept. Smlar to the maxmal pattern mnng concept, the closed pattern mnng concept was proposed to reduce the number of patterns generated. Closed pattern mnng algorthms, such as CLOSET[6] and FP-close[5], mne only closed frequent patterns. Defnton2 (Closed Frequent pattern) A pattern X s defned as a closed frequent pattern ff the followng two condtons are satsfed smultaneously: (1) The support value of X s hgher than mn_sup. (2) There exsts no pattern X whose support value s hgher than mn_sup, where X s a superset of X and s ncluded n all the transactons that nclude X..2.. Top-k Mnng concept. Generally, t s dffcult to predct how many frequent patterns wll be mned from a user-specfed mn_sup. If mn_sup s low, a huge number of frequent patterns are mned. On the other hand, f the mn_sup s large, a small number of frequent patterns are mned. Thus, t s dffcult for users to decde the mn_sup value. To avod such dffcultes, the Top-k mnng concept was proposed to mne the most k frequent patterns wth descendng order of support wthout specfyng mn_sup[7][8]. 4. Proposed Algorthm 4.1. Problems wth Conventonal Algorthms The Top-k mnng concept s mportant to enhance usablty for real applcatons for data mnng. However, the Top-k mnng concept stll requres a threshold k and users must decde the value of k before ntatng mnng Overvew of the proposed algorthm Our proposed algorthm, TF 2 P-growth, mnes patterns wth descendng order of support values wthout specfyng any thresholds. Then, t returns frequent patterns to users sequentally wth short response tmes. Frst, n secton 4., we propose Top-k FP-growth, whch s a Top-k mnng concept algorthm extended from FP-growth. Second, n secton 4.4, we propose TF 2 P- growth based on Top-k FP-growth. 4.. Top-k FP-growth Our proposed Top-k FP-growth algorthm s the base algorthm of TF 2 P-growth. Ths algorthm generates Topk patterns wthout a threshold of mn_sup but wth a threshold k value Extenson from FP-growth. To reduce addtonal computaton, we extended FP-growth for Top-k FPgrowth n three ponts, a) settng the nternal threshold Border_sup, b) reducng the number of patterns generated from the FP-tree, and c) outputtng frequent patterns. a) Settng of Border_sup Defnton (Border_sup) Border_sup s defned as the support value of k-th frequent 1-temset. Ths means that there are at least k 1-temsets wth support values hgher than Border_sup. Top-k FP-growth constructs an FP-tree usng Border_sup as a threshold. Border_sup s an nternal threshold and ts value s defned automatcally. Thus, users do not have to be concerned wth Border_sup. In concrete terms, frequent tems, whch are the prmtves of FP-tree constructon, are 1-temsets whose support values are hgher than Border_sup. [Lemma4.1] If the support value of 1-temsett s lower than Border_sup, t cannot be used to generate most k frequent patterns. [Ratonale] Let α be any 1-temset whose support s lower than Border_sup. Let β be any temset. Then, the followng expresson s satsfed. sup({ α, β}) sup({ α}) < Border_sup The above expresson shows that the support values of any temsets ncludng the 1-temset whose support value s lower than Border_sup are lower than Border_sup. In addton, t s clear that the number of temsets whose support values are hgher than Border_sup s more than k, based on the defnton of Border_sup. Thus, we are able to lmt the number of frequent 1-temsets that are prmtves of the FP-tree to the number of 1-temsets whose support values are more than Border_sup. For example, gven the TDB shown n Table 1, wth k = 6, Border_sup s because the support value of the 6-th frequent 1-temset s. Thus, the prmtves of the FP-tree becomes f, c, a, b, m, and p. b) Reducng the number of patterns generated from the FP-tree Pattern generaton from the constructed FP-tree by traversng all tems node-lnk drves more than k patterns.

5 To reduce both the number of patterns generated and the number of traversng node-lnks, a Reducton Array s adopted n Top-k FP-growth as shown n Fgure 4. Top-k FP-growth stores both the patterns generated from the FPtree and ther support values sequentally nto a Reducton Array. The prmtves of the Reducton Array are sorted by descendng order of ther support values after every traversal of one node-lnk. Defnton 4 (Boundary_sup) Boundary_sup s the support value of the stored k-th pattern n the Reducton Array. Intally, Boundary_sup s set to 0, but ts value ncreases after the generaton of k patterns from an FPtree. After traversng the node-lnk of each tem α n an FPtree, but before traversng the node-lnk of the next tem β n the FP-tree, Top-k FP-growth compares the support value of tem β ( = sup( β )) wth Boundary_sup. If the expresson sup(β ) < Boundary_sup s satsfed, t termnates pattern generaton from the FP-tree. On the other hand, f the expresson sup(β ) < Boundary_sup s not satsfed, t contnues pattern generaton from the FPtree. The reason why traversal of node-lnks s termnated f the support value of the next tem s lower than Boundary_sup s descrbed below. None of the patterns generated after traversng the node-lnk of tem β have support values hgher than sup(β ). Thus, f the expresson sup(β ) < Boundary_sup s satsfed, no patterns ncludng tem β have support values that are hgher than Boundary_sup. Moreover, every tem γ located under the tem β n the FP-tree s header table and all of the patterns generated by traversng the node-lnk of tem γ have support values lower than Boundary_sup. Thus, Top-k FP-growth can generate Top-k patterns n proporton even f the generaton of patterns from the FP-tree termnates when the expresson sup(β ) < Boundary_sup s satsfed. An example of the Reducton Array s shown n Fgure 4. Fgure 4 shows a Reducton Array after traversal of the node-lnk of an tem a, gven the TDB shown n Table 1 wth k=6. In Fgure 4, as the support value of the 6-th pattern {c, a} s, Boundary_sup s defned as. Before traversng the next node-lnk of an tem {b}, Top-k FPgrowth compares sup(b) wth Boundary_sup. In ths case, as sup(b ) equals Boundary_sup, Top-k FP-growth contnues traversng the node-lnk of tem {b}. k=6 Reducton Array Fgure 4. Example of a Reducton Array c) Output of frequent patterns Even f we adopt both extenson a) and extenson b), the number of frequent patterns generated from the FPtree s stll greater than k. Thus, Top-k FP-growth outputs the most k frequent patterns referrng to the Reducton Array by descendng order of ther support values Top-k FP-growth Algorthm. The Top-k FPgrowth algorthm s shown below. INPUT TDB k (number of frequent patterns) OUTPUT most k frequent patterns (descendng order) METHOD 1. Scan TDB, count support of all 1-temsets. 2. Set Border_sup to the support value of k-th 1-temset (descendng order). Then, generate an F-lst.. Construct an FP-tree accordng to the F-lst. 4. Generate frequent patterns from the FP-tree. Durng generaton, at every traversal of a node-lnk, recalculate Boundary_sup for reducton of the number of patterns generated. 5. Output the most k frequent patterns among generated patterns from the FP-tree, referrng to the Reducton Array TF 2 P-growth Boundary_sup= Header Table Item Lke other Top-k mnng concept algorthms, users stll have to specfy the value k before executon of Top-k FPgrowth proposed n 4.. To resolve ths problem, n ths secton we propose TF 2 P-growth based on Top-k FPgrowth. TF 2 P-growth mnes frequent patterns wth descendng order of support values wthout specfyng any thresholds. The mned frequent patterns are output to users every n c -patterns where n c s the chunk sze. By default, n c s set to The process of the TF 2 P-growth algorthm descrbed below. Users ntate TF 2 P-growth wthout specfyng any thresholds. Then, t sequentally returns the Top 1000 f c a b m p Sup 4 4 compare Head m:1 c:2 a:2 {} f: c:1 p:1 m:1 p:1 1 Users may change the chunk sze n to any number.

6 patterns, Top 1001 to 2000 patterns, Top 2001 to 000 patterns, etc. Once users have receved the Top 1000 patterns, they can ntate nterpretaton of these patterns. When the user s satsfed wth the mned frequent patterns, they can termnate mnng, or they may termnate mnng whenever they want. As the ntal results are the Top-n c patterns, whch can be set to a small number, t s possble to shorten the response tme from ntaton of the mnng untl generaton of the frst part of the results TF 2 P-growth algorthm. The TF 2 P-growth algorthm s shown below. INPUT TDB OUTPUT frequent patterns (descendng order of support) METHOD 1. Scan TDB to count support of all 1-temsets. 2. Set to 1.. Set n to n c, where n c s 1000 by default. 4. Set Border_sup to the support value of the n-th 1- temset (descendng order). Then, generate an F-lst. 5. Construct an FP-tree accordng to the F-lst. 6. Generate frequent patterns from the FP-tree. Durng generaton, at every node-lnk traversal, re-calculate Boundary_sup for reducton of the number of patterns generated. 7. Output the (n c ( -1)+1)th to (n c )th frequent patterns among the patterns generated from the FPtree. 8. Increment, then go to. 5. Performance Evaluaton In ths secton, we present performance evaluatons of TF 2 P-growth. We evaluated TF 2 P-growth wth regard to (1) comparson of the executon tme of FP-growth, Top-k FP-growth, and TF 2 P-growth, and (2) scalablty of TF 2 P- growth. We used T10I4D1000k, by IBM quest synthetc data generaton code[11], as a dataset. All of the experments were performed on a 2.4 GHz Pentum4 PC machne wth 1 GB of man memory, runnng RedHat Lnux 9.0, kernel verson All of the programs were wrtten n C++ and compled wth gcc Comparson of Executon tme We compared the performance of TF 2 P-growth wth both FP-growth and Top-k FP-growth. In real data mnng applcatons, users have dffculty n settng the mn_sup or the value k for the frst tme. Therefore, users must execute frequent pattern mnng recursvely changng the mn_sup or the value of k. Number of Freq. Patterns (patterns) TF^2P-growth Top k FP-growth (execute recursvely wth 1000 ntervals of the value k) FP-growth (execute recursvely wth 0.01% ntervals of the mn_sup) FP-growth (execute recursvely wth 0.05% ntervals of the mn_sup) Tme (sec) Fgure 5. Executon Tme vs. Mned Frequent Patterns In ths evaluaton, we measured the executon tme of FP-growth and Top-k FP-growth n the followng manners. In the case of FP-growth whose threshold s the mn_sup, we measured the executon tme n two patterns executng FP-growth recursvely n the range of 0.% mn_sup 0.1% wth an nterval equal to (1) 0.01% and (2) 0.05%. In the case of Top-k FP-growth whose threshold s the value k, we measured the executon tme n the followng pattern executng Top-k FP-growth recursvely by changng the value k from 1,000 to 10,000 wth an nterval of The expermental results are shown n Fgure 5. Frst, as t s dffcult to set the approprate mn_sup when users execute FP-growth, mn_sup must ntally be set hgh. Then, users can decrease the value of mn_sup slowly, e.g., wth an nterval of 0.01%. However, ths results n slow generaton of frequent patterns. On the other hand, usng TF 2 P-growth, users can obtan larger numbers of frequent patterns n the same tme n comparson wth usng FP-growth 2. Second, usng TF 2 P-growth, users can obtan large numbers of frequent patterns n the equvalent executon tme n comparson wth usng Top-k FP-growth recursvely. Ths s because TF 2 P-growth reduces the number of TDB scans by reusng the F-lst generated n the frst cycle of Top-k FP-growth. These expermental results ndcate that use of TF 2 P- growth has the advantage of requrng no threshold to be set. Moreover, users can obtan more frequent patterns n the equvalent executon tme n comparson wth usng FP-growth or Top-k FP-growth. 2 If users could know a relaton of mn_sup and the number of mned frequent patterns before the ntaton of mnng, FP-growth drves much more patterns n comparson to TF 2 P-growth such as the result of the nterval 0.05% n Fgure 5. However, t s general that users don t know the relaton.

7 Number of Freq. Patterns (patterns) Tme (sec) T10I4D1000k T10I4D5000k T10I4D10000k Fgure 6. Executon tme wth dfferent numbers of transactons 5.2. Scalablty of TF 2 P-growth We also evaluated the scalablty of TF 2 P-growth. In ths evaluaton, we prepared the same dataset wth dfferent numbers of transactons, T10I4D5000k and T10I4D10000k. The expermental results are shown n Fgure 6. As shown n Fgure 6, the executon tme of TF 2 P- growth ncreases lnearly as the dataset sze ncreases. Ths means that even f users apply TF 2 P-growth for a very large dataset, they can obtan the Top 1000 patterns and can avod the stuaton where they may obtan only a small number of frequent patterns after a long computaton tme. 6. Conclusons In ths paper, we proposed a new frequent pattern mnng algorthm, called TF 2 P-growth, whch mnes frequent patterns n descendng order of support wthout specfyng any threshold values. By applyng the proposed algorthm to the dataset T10I4D1000k, we confrmed that the followng two advantages. Frst, users can execute the mnng process wthout specfyng any threshold values. Second, users can mne more frequent patterns n comparson wth those mned by executng FP-growth recursvely wth changng mn_sup. For example, TF 2 P- growth mnes the 9,000 most frequent pattern twce as fast as FP-growth executed recursvely by changng mn_sup from 0.% to 0.1% wth 0.01% ntervals. Future work wll nvolve adoptng the maxmal or closed pattern mnng concept nto the proposed algorthm, and parallelzng the proposed algorthm. Acknowledgments Ths research was funded n part by both e-socety: the Comprehensve Development Foundaton Software of MEXT (Mnstry of Educaton, Culture, Sports, Scence, and Technology) and 21-century COE Programs: ICT Productve Academa of MEXT. References [1] R.Agrawl and R.Srkant, Fast Algorthms for Mnng Assocaton Rules, In Proc. of VLDB 94, pp , Santago, Chle, Sept [2] J. Han, J. Pe and P.S. Yu, Mnng Frequent Patterns wthout Canddate Generaton, In Proc. of the ACM SIGMOD Conference on Management of Data, pp.1-12, [] R.J.Bayard, Effcently Mnng Long Patterns from Databases, In Proc. of the ACM SIGMOD Conference on Management of Data, pp. 85-9, [4] G.Grahne and J.Zhu, Hgh Performance Mnng of Maxmal Frequent Itemsets, In Proc. of SIAM 0 Workshop on Hgh Performance Data Mnng, 200. [5] G. Grahne and J. Zhu, Effcently Usng Prefx-trees n Mnng Frequent Itemsets, In Proc. of the IEEE ICDM Workshop on Frequent Itemset Mnng Implementatons, 200. [6] J.Pe, J.Han and R.Mao, CLOSET: An Effcent Algorthm for Mnng Frequent Closed Itemsets, In Proc. of DMKD 00, [7] A.W.-C Fu., R.W.-W. Kwong and J.Tang, Mnng N-most Interestng Itemsets, In Proc. of the ISMIS 00, [8] J. Han, J. Wang, Y. Lu and P. Tzvetkov, Mnng Top-k Frequent Closed Patterns wthout Mnmum Support, In Proc. of IEEE ICDM Conference on Data Mnng, [9] J.S. Park, M.Chen, P.S. Yu, An effectve hash-based algorthms for mnng assocaton rules, In Proc. of the ACM SIGMOD Conference on Management of Data, pp , [10] A. Savasere, E. Omecnsk, and S. Navathe, An Effcent Algorthm for Mnng Assocaton Rules n Large Databases, In Proc. of VLDB 95, pp , [11] IBM Quest Data Mnng Project. Quest synthetc data generaton code. /syndata.html

Concurrent Apriori Data Mining Algorithms

Concurrent Apriori Data Mining Algorithms Concurrent Apror Data Mnng Algorthms Vassl Halatchev Department of Electrcal Engneerng and Computer Scence York Unversty, Toronto October 8, 2015 Outlne Why t s mportant Introducton to Assocaton Rule Mnng

More information

Available online at Available online at Advanced in Control Engineering and Information Science

Available online at   Available online at   Advanced in Control Engineering and Information Science Avalable onlne at wwwscencedrectcom Avalable onlne at wwwscencedrectcom Proceda Proceda Engneerng Engneerng 00 (2011) 15000 000 (2011) 1642 1646 Proceda Engneerng wwwelsevercom/locate/proceda Advanced

More information

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto

More information

Module Management Tool in Software Development Organizations

Module Management Tool in Software Development Organizations Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,

More information

Algorithms for Frequent Pattern Mining of Big Data

Algorithms for Frequent Pattern Mining of Big Data Research Inda Publcatons. http://www.rpublcaton.com Algorthms for Frequent Pattern Mnng of Bg Data Syed Zubar Ahmad Shah 1, Mohammad Amjad 1, Ahmad Al Habeeb 2, Mohd Huzafa Faruqu 1 and Mudasr Shaf 3 1

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points; Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features

More information

Virtual Machine Migration based on Trust Measurement of Computer Node

Virtual Machine Migration based on Trust Measurement of Computer Node Appled Mechancs and Materals Onlne: 2014-04-04 ISSN: 1662-7482, Vols. 536-537, pp 678-682 do:10.4028/www.scentfc.net/amm.536-537.678 2014 Trans Tech Publcatons, Swtzerland Vrtual Machne Mgraton based on

More information

Association Rule Mining with Parallel Frequent Pattern Growth Algorithm on Hadoop

Association Rule Mining with Parallel Frequent Pattern Growth Algorithm on Hadoop Assocaton Rule Mnng wth Parallel Frequent Pattern Growth Algorthm on Hadoop Zhgang Wang 1,2, Guqong Luo 3,*,Yong Hu 1,2, ZhenZhen Wang 1 1 School of Software Engneerng Jnlng Insttute of Technology Nanng,

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework

Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework Fuzzy Weghted Assocaton Rule Mnng wth Weghted Support and Confdence Framework M. Sulaman Khan, Maybn Muyeba, Frans Coenen 2 Lverpool Hope Unversty, School of Computng, Lverpool, UK 2 The Unversty of Lverpool,

More information

Discovering Relational Patterns across Multiple Databases

Discovering Relational Patterns across Multiple Databases Dscoverng Relatonal Patterns across Multple Databases Xngquan Zhu, 3 and Xndong Wu Dept. of Computer Scence & Eng., Florda Atlantc Unversty, Boca Raton, FL 3343, USA Dept. of Computer Scence, Unversty

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

More information

ApproxMGMSP: A Scalable Method of Mining Approximate Multidimensional Sequential Patterns on Distributed System

ApproxMGMSP: A Scalable Method of Mining Approximate Multidimensional Sequential Patterns on Distributed System ApproxMGMSP: A Scalable Method of Mnng Approxmate Multdmensonal Sequental Patterns on Dstrbuted System Changha Zhang, Kongfa Hu, Zhux Chen, Lng Chen Department of Computer Scence and Engneerng, Yangzhou

More information

Parallel and Distributed Association Rule Mining - Dr. Giuseppe Di Fatta. San Vigilio,

Parallel and Distributed Association Rule Mining - Dr. Giuseppe Di Fatta. San Vigilio, Parallel and Dstrbuted Assocaton Rule Mnng - Dr. Guseppe D Fatta fatta@nf.un-konstanz.de San Vglo, 18-09-2004 1 Overvew Assocaton Rule Mnng (ARM) Apror algorthm Hgh Performance Parallel and Dstrbuted Computng

More information

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng

More information

FINDING IMPORTANT NODES IN SOCIAL NETWORKS BASED ON MODIFIED PAGERANK

FINDING IMPORTANT NODES IN SOCIAL NETWORKS BASED ON MODIFIED PAGERANK FINDING IMPORTANT NODES IN SOCIAL NETWORKS BASED ON MODIFIED PAGERANK L-qng Qu, Yong-quan Lang 2, Jng-Chen 3, 2 College of Informaton Scence and Technology, Shandong Unversty of Scence and Technology,

More information

Transaction-Consistent Global Checkpoints in a Distributed Database System

Transaction-Consistent Global Checkpoints in a Distributed Database System Proceedngs of the World Congress on Engneerng 2008 Vol I Transacton-Consstent Global Checkponts n a Dstrbuted Database System Jang Wu, D. Manvannan and Bhavan Thurasngham Abstract Checkpontng and rollback

More information

Scheduling Remote Access to Scientific Instruments in Cyberinfrastructure for Education and Research

Scheduling Remote Access to Scientific Instruments in Cyberinfrastructure for Education and Research Schedulng Remote Access to Scentfc Instruments n Cybernfrastructure for Educaton and Research Je Yn 1, Junwe Cao 2,3,*, Yuexuan Wang 4, Lanchen Lu 1,3 and Cheng Wu 1,3 1 Natonal CIMS Engneerng and Research

More information

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory Background EECS. Operatng System Fundamentals No. Vrtual Memory Prof. Hu Jang Department of Electrcal Engneerng and Computer Scence, York Unversty Memory-management methods normally requres the entre process

More information

The Research of Support Vector Machine in Agricultural Data Classification

The Research of Support Vector Machine in Agricultural Data Classification The Research of Support Vector Machne n Agrcultural Data Classfcaton Le Sh, Qguo Duan, Xnmng Ma, Me Weng College of Informaton and Management Scence, HeNan Agrcultural Unversty, Zhengzhou 45000 Chna Zhengzhou

More information

Outline. CHARM: An Efficient Algorithm for Closed Itemset Mining. Introductions. Introductions

Outline. CHARM: An Efficient Algorithm for Closed Itemset Mining. Introductions. Introductions CHARM: An Effcent Algorthm for Closed Itemset Mnng Authors: Mohammed J. Zak and Chng-Ju Hsao Presenter: Junfeng Wu Outlne Introductons Itemset-Tdset tree CHARM algorthm Performance study Concluson Comments

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

Fast Computation of Shortest Path for Visiting Segments in the Plane

Fast Computation of Shortest Path for Visiting Segments in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 4 The Open Cybernetcs & Systemcs Journal, 04, 8, 4-9 Open Access Fast Computaton of Shortest Path for Vstng Segments n the Plane Ljuan Wang,, Bo Jang

More information

Load Balancing for Hex-Cell Interconnection Network

Load Balancing for Hex-Cell Interconnection Network Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,

More information

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

Conditional Speculative Decimal Addition*

Conditional Speculative Decimal Addition* Condtonal Speculatve Decmal Addton Alvaro Vazquez and Elsardo Antelo Dep. of Electronc and Computer Engneerng Unv. of Santago de Compostela, Span Ths work was supported n part by Xunta de Galca under grant

More information

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance Tsnghua Unversty at TAC 2009: Summarzng Mult-documents by Informaton Dstance Chong Long, Mnle Huang, Xaoyan Zhu State Key Laboratory of Intellgent Technology and Systems, Tsnghua Natonal Laboratory for

More information

A Heuristic for Mining Association Rules In Polynomial Time*

A Heuristic for Mining Association Rules In Polynomial Time* Complete reference nformaton: Ylmaz, E., E. Trantaphyllou, J. Chen, and T.W. Lao, (3), A Heurstc for Mnng Assocaton Rules In Polynomal Tme, Computer and Mathematcal Modellng, No. 37, pp. 9-33. A Heurstc

More information

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1) Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A

More information

USING GRAPHING SKILLS

USING GRAPHING SKILLS Name: BOLOGY: Date: _ Class: USNG GRAPHNG SKLLS NTRODUCTON: Recorded data can be plotted on a graph. A graph s a pctoral representaton of nformaton recorded n a data table. t s used to show a relatonshp

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information

Parallel Closed Frequent Pattern Mining on PC Cluster

Parallel Closed Frequent Pattern Mining on PC Cluster DEWS2005 3C-i5 PC, 169-8555 3-4-1 169-8555 3-4-1 101-8430 2-1-2 E-mail: {eigo,hirate}@yama.info.waseda.ac.jp, yamana@waseda.jp FPclose PC 32 PC 2% 30.9 PC Parallel Closed Frequent Pattern Mining on PC

More information

Learning-Based Top-N Selection Query Evaluation over Relational Databases

Learning-Based Top-N Selection Query Evaluation over Relational Databases Learnng-Based Top-N Selecton Query Evaluaton over Relatonal Databases Lang Zhu *, Wey Meng ** * School of Mathematcs and Computer Scence, Hebe Unversty, Baodng, Hebe 071002, Chna, zhu@mal.hbu.edu.cn **

More information

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide Lobachevsky State Unversty of Nzhn Novgorod Polyhedron Quck Start Gude Nzhn Novgorod 2016 Contents Specfcaton of Polyhedron software... 3 Theoretcal background... 4 1. Interface of Polyhedron... 6 1.1.

More information

Determining Fuzzy Sets for Quantitative Attributes in Data Mining Problems

Determining Fuzzy Sets for Quantitative Attributes in Data Mining Problems Determnng Fuzzy Sets for Quanttatve Attrbutes n Data Mnng Problems ATTILA GYENESEI Turku Centre for Computer Scence (TUCS) Unversty of Turku, Department of Computer Scence Lemmnkäsenkatu 4A, FIN-5 Turku

More information

A Heuristic for Mining Association Rules In Polynomial Time

A Heuristic for Mining Association Rules In Polynomial Time A Heurstc for Mnng Assocaton Rules In Polynomal Tme E. YILMAZ General Electrc Card Servces, Inc. A unt of General Electrc Captal Corporaton 6 Summer Street, MS -39C, Stamford, CT, 697, U.S.A. egemen.ylmaz@gecaptal.com

More information

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Learning the Kernel Parameters in Kernel Minimum Distance Classifier Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department

More information

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

Enhancement of Infrequent Purchased Product Recommendation Using Data Mining Techniques

Enhancement of Infrequent Purchased Product Recommendation Using Data Mining Techniques Enhancement of Infrequent Purchased Product Recommendaton Usng Data Mnng Technques Noraswalza Abdullah, Yue Xu, Shlomo Geva, and Mark Loo Dscplne of Computer Scence Faculty of Scence and Technology Queensland

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information

Mathematics 256 a course in differential equations for engineering students

Mathematics 256 a course in differential equations for engineering students Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the

More information

Esc101 Lecture 1 st April, 2008 Generating Permutation

Esc101 Lecture 1 st April, 2008 Generating Permutation Esc101 Lecture 1 Aprl, 2008 Generatng Permutaton In ths class we wll look at a problem to wrte a program that takes as nput 1,2,...,N and prnts out all possble permutatons of the numbers 1,2,...,N. For

More information

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth

More information

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Інформаційні технології в освіті ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Some aspects of programmng educaton

More information

CMPS 10 Introduction to Computer Science Lecture Notes

CMPS 10 Introduction to Computer Science Lecture Notes CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not

More information

Hierarchical clustering for gene expression data analysis

Hierarchical clustering for gene expression data analysis Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally

More information

Innovation Typology. Collaborative Authoritativeness. Focused Web Mining. Text and Data Mining In Innovation. Generational Models

Innovation Typology. Collaborative Authoritativeness. Focused Web Mining. Text and Data Mining In Innovation. Generational Models Text and Data Mnng In Innovaton Joseph Engler Innovaton Typology Generatonal Models 1. Lnear or Push (Baroque) 2. Pull (Romantc) 3. Cyclc (Classcal) 4. Strategc (New Age) 5. Collaboratve (Polyphonc) Collaboratve

More information

A New Approach For the Ranking of Fuzzy Sets With Different Heights

A New Approach For the Ranking of Fuzzy Sets With Different Heights New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays

More information

CE 221 Data Structures and Algorithms

CE 221 Data Structures and Algorithms CE 1 ata Structures and Algorthms Chapter 4: Trees BST Text: Read Wess, 4.3 Izmr Unversty of Economcs 1 The Search Tree AT Bnary Search Trees An mportant applcaton of bnary trees s n searchng. Let us assume

More information

On Some Entertaining Applications of the Concept of Set in Computer Science Course

On Some Entertaining Applications of the Concept of Set in Computer Science Course On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,

More information

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach Data Representaton n Dgtal Desgn, a Sngle Converson Equaton and a Formal Languages Approach Hassan Farhat Unversty of Nebraska at Omaha Abstract- In the study of data representaton n dgtal desgn and computer

More information

A User Selection Method in Advertising System

A User Selection Method in Advertising System Int. J. Communcatons, etwork and System Scences, 2010, 3, 54-58 do:10.4236/jcns.2010.31007 Publshed Onlne January 2010 (http://www.scrp.org/journal/jcns/). A User Selecton Method n Advertsng System Shy

More information

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications 14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of

More information

Life Tables (Times) Summary. Sample StatFolio: lifetable times.sgp

Life Tables (Times) Summary. Sample StatFolio: lifetable times.sgp Lfe Tables (Tmes) Summary... 1 Data Input... 2 Analyss Summary... 3 Survval Functon... 5 Log Survval Functon... 6 Cumulatve Hazard Functon... 7 Percentles... 7 Group Comparsons... 8 Summary The Lfe Tables

More information

CSE 326: Data Structures Quicksort Comparison Sorting Bound

CSE 326: Data Structures Quicksort Comparison Sorting Bound CSE 326: Data Structures Qucksort Comparson Sortng Bound Steve Setz Wnter 2009 Qucksort Qucksort uses a dvde and conquer strategy, but does not requre the O(N) extra space that MergeSort does. Here s the

More information

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

More information

Meta-heuristics for Multidimensional Knapsack Problems

Meta-heuristics for Multidimensional Knapsack Problems 2012 4th Internatonal Conference on Computer Research and Development IPCSIT vol.39 (2012) (2012) IACSIT Press, Sngapore Meta-heurstcs for Multdmensonal Knapsack Problems Zhbao Man + Computer Scence Department,

More information

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana

More information

A Combined Approach for Mining Fuzzy Frequent Itemset

A Combined Approach for Mining Fuzzy Frequent Itemset A Combned Approach for Mnng Fuzzy Frequent Itemset R. Prabamaneswar Department of Computer Scence Govndammal Adtanar College for Women Truchendur 628 215 ABSTRACT Frequent Itemset Mnng s an mportant approach

More information

Array transposition in CUDA shared memory

Array transposition in CUDA shared memory Array transposton n CUDA shared memory Mke Gles February 19, 2014 Abstract Ths short note s nspred by some code wrtten by Jeremy Appleyard for the transposton of data through shared memory. I had some

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

TECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS. Muradaliyev A.Z.

TECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS. Muradaliyev A.Z. TECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS Muradalyev AZ Azerbajan Scentfc-Research and Desgn-Prospectng Insttute of Energetc AZ1012, Ave HZardab-94 E-mal:aydn_murad@yahoocom Importance of

More information

A Resources Virtualization Approach Supporting Uniform Access to Heterogeneous Grid Resources 1

A Resources Virtualization Approach Supporting Uniform Access to Heterogeneous Grid Resources 1 A Resources Vrtualzaton Approach Supportng Unform Access to Heterogeneous Grd Resources 1 Cunhao Fang 1, Yaoxue Zhang 2, Song Cao 3 1 Tsnghua Natonal Labatory of Inforamaton Scence and Technology 2 Department

More information

A METHOD FOR FACTOR SCREENING OF SIMULATION EXPERIMENTS BASED ON ASSOCIATION RULE MINING

A METHOD FOR FACTOR SCREENING OF SIMULATION EXPERIMENTS BASED ON ASSOCIATION RULE MINING A METHOD FOR FACTOR SCREENING OF SIMULATION EXPERIMENTS BASED ON ASSOCIATION RULE MINING Lngyun Lu (a), We L (b), Png Ma (c), Mng Yang (d) Control and Smulaton Center, Harbn Insttute of Technology, Harbn

More information

A Similarity Measure Method for Symbolization Time Series

A Similarity Measure Method for Symbolization Time Series Research Journal of Appled Scences, Engneerng and Technology 5(5): 1726-1730, 2013 ISSN: 2040-7459; e-issn: 2040-7467 Maxwell Scentfc Organzaton, 2013 Submtted: July 27, 2012 Accepted: September 03, 2012

More information

CS1100 Introduction to Programming

CS1100 Introduction to Programming Factoral (n) Recursve Program fact(n) = n*fact(n-) CS00 Introducton to Programmng Recurson and Sortng Madhu Mutyam Department of Computer Scence and Engneerng Indan Insttute of Technology Madras nt fact

More information

CSE 326: Data Structures Quicksort Comparison Sorting Bound

CSE 326: Data Structures Quicksort Comparison Sorting Bound CSE 326: Data Structures Qucksort Comparson Sortng Bound Bran Curless Sprng 2008 Announcements (5/14/08) Homework due at begnnng of class on Frday. Secton tomorrow: Graded homeworks returned More dscusson

More information

ELEC 377 Operating Systems. Week 6 Class 3

ELEC 377 Operating Systems. Week 6 Class 3 ELEC 377 Operatng Systems Week 6 Class 3 Last Class Memory Management Memory Pagng Pagng Structure ELEC 377 Operatng Systems Today Pagng Szes Vrtual Memory Concept Demand Pagng ELEC 377 Operatng Systems

More information

Performance Evaluation of Information Retrieval Systems

Performance Evaluation of Information Retrieval Systems Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence

More information

Maintaining temporal validity of real-time data on non-continuously executing resources

Maintaining temporal validity of real-time data on non-continuously executing resources Mantanng temporal valdty of real-tme data on non-contnuously executng resources Tan Ba, Hong Lu and Juan Yang Hunan Insttute of Scence and Technology, College of Computer Scence, 44, Yueyang, Chna Wuhan

More information

Efficient Distributed File System (EDFS)

Efficient Distributed File System (EDFS) Effcent Dstrbuted Fle System (EDFS) (Sem-Centralzed) Debessay(Debsh) Fesehaye, Rahul Malk & Klara Naherstedt Unversty of Illnos-Urbana Champagn Contents Problem Statement, Related Work, EDFS Desgn Rate

More information

THE CONDENSED FUZZY K-NEAREST NEIGHBOR RULE BASED ON SAMPLE FUZZY ENTROPY

THE CONDENSED FUZZY K-NEAREST NEIGHBOR RULE BASED ON SAMPLE FUZZY ENTROPY Proceedngs of the 20 Internatonal Conference on Machne Learnng and Cybernetcs, Guln, 0-3 July, 20 THE CONDENSED FUZZY K-NEAREST NEIGHBOR RULE BASED ON SAMPLE FUZZY ENTROPY JUN-HAI ZHAI, NA LI, MENG-YAO

More information

A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems

A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems A Unfed Framework for Semantcs and Feature Based Relevance Feedback n Image Retreval Systems Ye Lu *, Chunhu Hu 2, Xngquan Zhu 3*, HongJang Zhang 2, Qang Yang * School of Computng Scence Smon Fraser Unversty

More information

Assembler. Building a Modern Computer From First Principles.

Assembler. Building a Modern Computer From First Principles. Assembler Buldng a Modern Computer From Frst Prncples www.nand2tetrs.org Elements of Computng Systems, Nsan & Schocken, MIT Press, www.nand2tetrs.org, Chapter 6: Assembler slde Where we are at: Human Thought

More information

Optimization of integrated circuits by means of simulated annealing. Jernej Olenšek, Janez Puhan, Árpád Bűrmen, Sašo Tomažič, Tadej Tuma

Optimization of integrated circuits by means of simulated annealing. Jernej Olenšek, Janez Puhan, Árpád Bűrmen, Sašo Tomažič, Tadej Tuma Optmzaton of ntegrated crcuts by means of smulated annealng Jernej Olenšek, Janez Puhan, Árpád Bűrmen, Sašo Tomažč, Tadej Tuma Unversty of Ljubljana, Faculty of Electrcal Engneerng, Tržaška 25, Ljubljana,

More information

Boundary-Based Time Series Sorting

Boundary-Based Time Series Sorting JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA, VOL. 6, NO. 3, SEPTEMBER 2008 323 Boundary-Based Tme Seres Sortng Jun-Ku L, Yuan-Zhen Wang, and Ha-Bo L Abstract In many applcatons, t s desrable

More information

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and

More information

Analysis of Non-coherent Fault Trees Using Ternary Decision Diagrams

Analysis of Non-coherent Fault Trees Using Ternary Decision Diagrams Analyss of Non-coherent Fault Trees Usng Ternary Decson Dagrams Rasa Remenyte-Prescott Dep. of Aeronautcal and Automotve Engneerng Loughborough Unversty, Loughborough, LE11 3TU, England R.Remenyte-Prescott@lboro.ac.uk

More information

Deep Classification in Large-scale Text Hierarchies

Deep Classification in Large-scale Text Hierarchies Deep Classfcaton n Large-scale Text Herarches Gu-Rong Xue Dkan Xng Qang Yang 2 Yong Yu Dept. of Computer Scence and Engneerng Shangha Jao-Tong Unversty {grxue, dkxng, yyu}@apex.sjtu.edu.cn 2 Hong Kong

More information

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

Classifier Selection Based on Data Complexity Measures *

Classifier Selection Based on Data Complexity Measures * Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.

More information

3D Virtual Eyeglass Frames Modeling from Multiple Camera Image Data Based on the GFFD Deformation Method

3D Virtual Eyeglass Frames Modeling from Multiple Camera Image Data Based on the GFFD Deformation Method NICOGRAPH Internatonal 2012, pp. 114-119 3D Vrtual Eyeglass Frames Modelng from Multple Camera Image Data Based on the GFFD Deformaton Method Norak Tamura, Somsangouane Sngthemphone and Katsuhro Ktama

More information

User Authentication Based On Behavioral Mouse Dynamics Biometrics

User Authentication Based On Behavioral Mouse Dynamics Biometrics User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA

More information

BIN XIA et al: AN IMPROVED K-MEANS ALGORITHM BASED ON CLOUD PLATFORM FOR DATA MINING

BIN XIA et al: AN IMPROVED K-MEANS ALGORITHM BASED ON CLOUD PLATFORM FOR DATA MINING An Improved K-means Algorthm based on Cloud Platform for Data Mnng Bn Xa *, Yan Lu 2. School of nformaton and management scence, Henan Agrcultural Unversty, Zhengzhou, Henan 450002, P.R. Chna 2. College

More information

S1 Note. Basis functions.

S1 Note. Basis functions. S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type

More information

Solving two-person zero-sum game by Matlab

Solving two-person zero-sum game by Matlab Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by

More information

A Clustering Algorithm for Chinese Adjectives and Nouns 1

A Clustering Algorithm for Chinese Adjectives and Nouns 1 Clusterng lgorthm for Chnese dectves and ouns Yang Wen, Chunfa Yuan, Changnng Huang 2 State Key aboratory of Intellgent Technology and System Deptartment of Computer Scence & Technology, Tsnghua Unversty,

More information

An Efficient Genetic Algorithm with Fuzzy c-means Clustering for Traveling Salesman Problem

An Efficient Genetic Algorithm with Fuzzy c-means Clustering for Traveling Salesman Problem An Effcent Genetc Algorthm wth Fuzzy c-means Clusterng for Travelng Salesman Problem Jong-Won Yoon and Sung-Bae Cho Dept. of Computer Scence Yonse Unversty Seoul, Korea jwyoon@sclab.yonse.ac.r, sbcho@cs.yonse.ac.r

More information

Ontology Generator from Relational Database Based on Jena

Ontology Generator from Relational Database Based on Jena Computer and Informaton Scence Vol. 3, No. 2; May 2010 Ontology Generator from Relatonal Database Based on Jena Shufeng Zhou (Correspondng author) College of Mathematcs Scence, Laocheng Unversty No.34

More information

PHYSICS-ENHANCED L-SYSTEMS

PHYSICS-ENHANCED L-SYSTEMS PHYSICS-ENHANCED L-SYSTEMS Hansrud Noser 1, Stephan Rudolph 2, Peter Stuck 1 1 Department of Informatcs Unversty of Zurch, Wnterthurerstr. 190 CH-8057 Zurch Swtzerland noser(stuck)@f.unzh.ch, http://www.f.unzh.ch/~noser(~stuck)

More information

Wireless Sensor Networks Fault Identification Using Data Association

Wireless Sensor Networks Fault Identification Using Data Association Journal of Computer Scence 8 (9): 1501-1505, 2012 ISSN 1549-3636 2012 Scence Publcatons Wreless Sensor Networks Fault Identfcaton Usng Data Assocaton 1 Abram Kongu, T., 2 P. Thangaraj and 1 P. Prakanth

More information

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems

More information

LinkSelector: A Web Mining Approach to. Hyperlink Selection for Web Portals

LinkSelector: A Web Mining Approach to. Hyperlink Selection for Web Portals nkselector: A Web Mnng Approach to Hyperlnk Selecton for Web Portals Xao Fang and Olva R. u Sheng Department of Management Informaton Systems Unversty of Arzona, AZ 8572 {xfang,sheng}@bpa.arzona.edu Submtted

More information

STING : A Statistical Information Grid Approach to Spatial Data Mining

STING : A Statistical Information Grid Approach to Spatial Data Mining STING : A Statstcal Informaton Grd Approach to Spatal Data Mnng We Wang, Jong Yang, and Rchard Muntz Department of Computer Scence Unversty of Calforna, Los Angeles {wewang, jyang, muntz}@cs.ucla.edu February

More information