Fequent Pattern Recognization From Stream Data Using Compact Data Structure

Transcription

1 Fqunt Pattrn Rcognization From Stram Data Using Compact Data Structur Fabin M Christian 1, Narndra C.Chauhan 2, Nilsh B. Prajapati 3 1 PG Scholar, CE Dpartmnt, BVM Engg. Collg, V.V.Nagar, fabin.christian@gmail.com 2 Assoc. Prof., IT Dpartmnt, A.D. Patl Engg. Collg, V.V.Nagar, narndracchauhan@gmail.com 3 Assoc. Prof., IT Dpartmnt, BVM Engg. Collg,V.V.Nagar, nilsh.prajapati@bvmnginring.co.in Abstract Mining frqunt pattrn from stram data is a challnging task. Finding frqunt pattrn from data strams hav bn found to b usful in many application such as stock markt prdiction, snsor data analysis, ntwork traffic analysis, -businss and tlcommunication data analysis. Frqunt Pattrn Stram tr [1] is usd for maintaining frqunt pattrn ovr a priod of tim using modifid FP tr algorithm. This approach maintains tiltd tim window at ach nod which consums largr spac. Compact Pattrn Stram Tr [2] assums that only currnt pattrns ar of importanc and uss sliding window protocol for maintaining it. This approach dos not giv importanc to past frqunt pattrns. Th aim of this rsarch papr is to combin th faturs of frqunt pattrn stram tr and Compact pattrn stram tr to find th frqunt pattrn ovr a priod of tim. Th rsarch assums th pattrns that ar infrqunt for a priod can bcom frqunt in th futur. It is proposd to gnrat a Compact Frqunt Pattrn Stram tr that maintains tiltd tim window at th tail nod and at partition nod that kp track of th pattrn ovr a priod of tim. Th pattrn whos support count is gratr than prdfind thrshold is considrd to b frqunt. Th proposd approach has provd advantag of rduction in spac utilization, compaction in spac utilization and considration of past pattrns that may bcom frqunt. Th systm will gnrat frqunt pattrn with compact data structur and in a tim fficint mannr. Indx Trms Frqunt pattrn stram tr, Compact pattrn stram tr, Dynamic stram tr tiltd tim window, sliding window 1. INTRODUCTION Frqunt pattrn has bn studid widly ovr a priod of tim but xtnding it to data stram is a challnging task. Data itms ar continuously flowing through th intrnt or snsor ntworks in applications lik ntwork monitoring and mssag dissmination [5]. Frqunt pattrn mining and its associatd mthods ar widly usd in association rul mining tasks, squntial pattrn mining, squntial pattrn mining, structurd pattrn mining, associativ classification and so on[1]. Rcntly, th incrasing prominnc of data strams has ld to th study of onlin mining of frqunt Itmsts, which is an important tchniqu that is ssntial to a wid rang of mrging applications, such as wb log and click-stram mining, ntwork traffic analysis, trnd analysis and fraud dtction in tlcommunications data, -businss and stock markt analysis, and snsor ntworks. With th rapid mrgnc of ths nw application domains, it has bcom incrasingly difficult to conduct advancd analysis and data mining ovr fast-arriving and larg data strams in ordr to captur intrsting trnds, pattrns and xcptions [5]. Mining frqunt pattrn from continuous data stram is mor difficult than finding frqunt pattrn from static databas. Th basic proprtis of data stram ar that thy ar continuous as thy arriv at fast rat. Scondly, data can only b rad only onc bcaus of continuity. Thirdly th total amount of data that ar arriving is unboundd. Mining strams rquir fast procssing so that fast data arrival rat can b maintaind and rsults of mining can b achivd in shortr rspons tim. Th mining of continuously arriving data can b don through tim bas sliding window approach. Thr ar thr important windows modls can b usd basd on fatur of th application landmark window modl, dampd window modl or sliding window modl. Landmark window prforms mining from data from a particular point calld landmark to th currnt tim. In th dampd All rights Rsrvd 1

2 modl arriving data is assignd various wights basd on thir arrival, nwly arriving data ar assignd highr wights than oldr data. In sliding window fixd window is maintaind and mining is applid within this window. 2. STREAM MINING MODELS Som papr that dscribs various mthods to find frqunt pattrn idntification from data strams ar dscribd and discussd hr. 2.1 Frqunt pattrn stram tr modl In th papr [1] a mthod for finding frqunt pattrn using FP stram tr is proposd. Compard to mining from a static transaction data st, th straming cas has far mor information to track and far gratr complxity to manag. Infrqunt itms can bcom frqunt latr on and hnc cannot b ignord. Th storag structur nds to b dynamically adjustd to rflct th volution of Itmsts frquncis ovr tim. In this papr, computing and maintaining all th frqunt pattrns (which is usually mor stabl and smallr than th straming data) and dynamically updating thm with th incoming data strams. Thy xtndd th framwork to min tim-snsitiv pattrns with approximat support guarant. Thy incrmntally maintain -tim windows for ach pattrn at multipl tim granularitis. Intrsting quris can b constructd and answrd undr this framwork. Morovr, inspird by th fact that th FP-tr provids an ffctiv data structur for frqunt pattrn mining, w dvlop FP-stram, an ffctiv FP-tr-basd modl for mining frqunt pattrns from data strams. An FP-stram structur consists of (a) an in-mmory frqunt pattrn-tr to captur th frqunt and sub-frqunt Itmsts information, and (b) a tiltd-tim window tabl for ach frqunt pattrn. Efficint algorithms for constructing, maintaining and updating an FP-stram structur ovr data strams ar xplord. 2.2 Compact pattrn stram modl In this papr [2] a mthod for finding pattrn from data stram using Compact pattrn stram tr. An fficint tchniqu to discovr th complt st of rcnt frqunt pattrns from a high-spd data stram ovr a sliding window has bn proposd. A Compact Pattrn Stram tr (CPS-tr) to captur th rcnt stram data contnt and fficintly rmov th obsolt, old stram data has bn dvlopd to provid compaction. This papr also introducs th concpt of dynamic tr rstructuring in our CPS-tr to produc a highly compact frquncy-dscnding tr structur at runtim. This causs th high frquncy pattrn to b maintaind at th highr lvl and low frquncy componnts ar mainly maintaind at th laf nods. Th complt st of rcnt frqunt pattrns is obtaind from th CPStr of th currnt window using an FP-growth mining tchniqu. 2.3 Classification and clustring modl In this papr [3] a mthod for stram classification is proposd. A nw nsmbl modl which combins both classifirs and clustrs togthr for mining data strams is proposd. Th main challngs of this nw nsmbl modl includ (1) clustrs formulatd from data strams only carry clustr IDs, with no gnuin class labl information, and (2) concpt drifting undrlying data strams maks it vn hardr to combin clustrs and classifirs into on nsmbl framwork. To handl challng (1) a labl propagation mthod to infr ach clustr s class labl by making full us of both class labl information from classifirs, and intrnal structur information from clustrs is proposd. To handl challng (2), w prsnt a nw wighting schma to wight all bas modls according to thir consistncis with th up-to-dat bas modl. As a rsult, all classifirs and clustrs can b combind togthr, through a wightd avrag mchanism, for prdiction. Du to its inhrnt mrits in handling drifting concpts and larg data volums, nsmbl larning has traditionally attractd many attntions in stram data mining rsarch. Nvrthlss, as labling training sampls is a labor intnsiv and xpnsiv procss, in a practical stram data mining scnario, it is oftn th cas that w may hav a vry fw labld training sampls (to build classifirs), but a larg numbr of unlabld sampls ar availabl to build clustrs. It would b a wast to only considr classifirs in an nsmbl modl, lik most xisting solutions do. Accordingly, in this papr, a nw nsmbl larning mthod which rlaxs th original nsmbl modls to accommodat both classifirs and clustrs through a wightd avrag mchanism. In ordr to handl concpt drifting problm, w also propos a nw consistncy-basd wighting schma to wight all bas modls, according to thir consistncis with rspct to th up-to-dat All rights Rsrvd 2

3 2.4 Sliding window modl In this papr [4] thy dvlopd a novl approach for mining frqunt Itmsts from data strams basd on a tim-snsitiv sliding window modl. Th approach consists of a storag structur that capturs all possibl frqunt Itmsts and a tabl providing approximat counts of th xpird data itms, whos siz can b adjustd by th availabl storag spac.. In addition, th approach guarants no fals alarm or no fals dismissal to th rsults yildd. Slf adjusting discounting tabl (SDT) for th limitd mmory is proposd. Th SDT prforms wll whn th availabl mmory is limitd. 2.5 Postrior probability basd classification modl In this papr [5] a mthod is proposd mthod for classification of stram data In this papr a nw approach to min data strams by stimating rliabl postrior probabilitis using an nsmbl of modls to match th distribution ovr undr-sampls of ngativs and rpatd sampls of positivs has bn proposd. Thy formally show som intrsting and important proprtis of th proposd framwork,.g., rliability of stimatd probabilitis on skwd positiv class, accuracy of stimatd probabilitis, fficincy and scalability. 2.6 Frqunt pattrn idntification modl basd on dynamic load balancing In this papr [6] a mthod is proposd for frqunt pattrn discovry from data strams In this papr, th practical problm of frqunt-itmsts discovry in data-stram nvironmnts which may suffr from data ovrload. Th main issus includ frqunt-pattrn mining and data-ovrload handling. Thrfor, a mining algorithm togthr with two ddicatd ovrload-handling mchanisms is proposd. Th algorithm xtracts basic information from straming data and kps th information in its data structur. Th mining task is accomplishd whn rqustd by calculating th approximat counts of Itmsts and thn rturning th frqunt ons. Whn thr xists data ovrload, on of th two mchanisms is xcutd to sttl th ovrload by ithr improving systm throughput or shdding data load. 2.7 Dynamic stram tr modl DSTr [8], which is a prfix-tr that is built basd on a canonical itm ordr, has rcntly bn proposd for mining an xact st of rcnt frqunt pattrns ovr a data stram using a sliding window tchniqu. In DSTr, th sliding window consists of svral batchs of transactions, and th transaction information for ach batch is xplicitly maintaind at ach nod in th tr structur. To discovr th complt st of rcnt frqunt pattrns, th FP-growth mining tchniqu is applid to th DSTr of th currnt window. Evn though th construction of th DSTr rquirs only on scan of th data stram, it cannot guarant that a high lvl of prfix sharing (lik in FP-tr) will b achivd in th tr structur bcaus th itms ar insrtd into th tr in a frquncy-indpndnt canonical ordr. Morovr, upon sliding of window th tr updat mchanism of DSTr may lav som garbag nods in th tr structur whn th mining rqust is dlayd. 3. PROPOSED APPROACH Assuming that our proposd systm architctur is alrady prsnt, w carry out rsarch rgarding frqunt pattrn from data stram. It combins th faturs of frqunt pattrn stram tr and Compact pattrn stram tr. Th frqunt pattrn stram tr maintains tiltd tim window at ach nod showing support count of th particular itm ovr a priod [1]. Th FP stram tr also maintains a pattrn tr showing support count of th itm in th currnt window. Th compact pattrn stram tr uss window basd approach in which th window is dividd into pan and ach pan constituts th transaction [2]. Th nods ar dividd into two typs 1) Ordinary nod storing th itm and th support count of th itm and 2) Tail nod and partition nod storing th support count of th itm as th information of th pan. Th support count of th itm is maintaind at ach nod. Th pan information is maintaind at th tail nod and partition nod. W could propos th working of our proposd modl in th following mannr: Th incoming data stram is dividd into sliding windows. Th siz of th sliding window nds to b spcifid by th usr. Th data stram is qually dividd according to th sliding window siz spcifid. Th compact FP Stram tr is constructd by maintaining tiltd tim window at th tail nod and at th partition nod. Th tiltd tim window consists of th support count of th pattrn as wll as th sliding windows in which th pattrn is occurrd. Th tiltd tim window consists All rights Rsrvd 3

4 this information ovr a priod of tim. Th ntir pattrn whos support count is abov th thrshold valu is considrd to b frqunt. Dpth first sarch is carrid out starting from th root nod to find th frqunt pattrn. Each tiltd tim window is chckd and th final output will contain th frqunt pattrn, th transaction window in which th pattrn is frqunt as wll as th support count of th frqunt pattrn. Th structur of Compact FP stram tr is shown in Figur 3.1. Th siz of th sliding window is assumd to b thr. Thus thr words form a singl transaction. Th root of th tr is a always a null nod. Th givn stram is papr, puddl and polythn. During th initial stp th count of ach and vry word is rcordd. Th words ar arrangd in dscnding ordr in th Tabl 3.1. Thus th maximum occuring is ntrd in th tabl first. Tabl 3.1 Tabl maintaining word count Word Count P 4 E 4 D 2 L 2 A 1 R 1 U 1 O 1 Y 1 T 1 H 1 N 1 {} p T1 3 p r T1 1 a d d l u T1 1 l o y t h n T1 1 Figur 3.1 Compact FP stram All rights Rsrvd 4

5 For sam pattrn papr, puddl and polythn th FP stram implmntation is as shown in Figur 3.4. Th sliding window siz is assumd to thr. In FP stram tr th tiltd tim window is to b maintaind at ach and vy nod. It is also ncssary to spcify th numbr of transaction in ach tiltd tim window. Th tabl showing word count in window for FP stram tr is shown in Tabl 3.1. As shown in Figur 3.2 th tiltd tim window in FP stram tr has to b maintaind at ach nod making th data structur complx. Morovr th siz of th TTW has to b spifid in advanc making th data structur static. Hr th TTW siz is assumd to b on. Thrshold is providd by th usr. Th pattrn whos support count is gratr than thrshold is said to b frqunt. {} p p a t11 t11 d d l r t11 t11 l u o y t h n Figur 3.2 FP stram tr All rights Rsrvd 5

6 4. PROPOSED ALGORITHM Th proposd Compact FP stram algorithm is as follows: Stp 1: Initializ Compact FP stram tr to b mpty. Stp 2: Whn th incoming stram ar accumulatd: (i) Th incoming stram data is dividd into qual sizd sliding window spcifid by th usr. (ii) Th tabl containing pattrn and suppot count is cratd. Th pattrn is arrangd in th tabl in dscnding ordr of its support count. (iii) Th compact FP stram tr is constructd by maintaining linkd list basd tiltd tim (iv) window at partition and tail nods. Th pattrns ar arrangd in th Compact FP stram tr according to its occuranc in th tabl. Stp3: Thrshold σ is takn from th usr. Stp 4: Dpth First travrsal is don starting from th root nod and th support count in ach linkd list basd tiltd tim window maintaind at tail nod and partition nod is chckd against th thrshold valu σ. Stp 5: All thos pattrn whos support count is gratr than thrshold σ is displayd along th window numbr and support count. 5. IMPLEMENTATION AND EXPERIMENTAL RESULTS 5.1 Data St Usd Snsor stram contains information (tmpratur, humidity, light, and snsor voltag) collctd from 54 snsors dployd in Intl Brkly rsarch lab. Th whol stram contains conscutiv information rcordd ovr a 2 months priod. Th snsor ID is th class labl, so th larning task of th stram is to corrctly idntify most frquntly occurring snsor ID purly basd on th snsor data and th corrsponding rcording tim. Th data st is in standard Comma Sparat Valu(CSV) format. Th aim is to find which class of Snsor ID is occurring most frquntly and controlling th tmpratur and humidity corrsponding to that rgion. Figur 5.1 shows th Snsor stram datast. Figur 5.1: Snsor Data st [13] 5.2 Implmntation Th rsults of applying th FP stram tr implmntation on Snsor data st is shown in Figur 5.2. Th thrshold is takn to b four and th sliding window siz is takn to b svn. Th first fild of th output provids th class nams which ar frqunt, th scond fild givs th transaction window numbr in which th itms is frqunt and th third fild givs th frquncy of th corrsponding frqunt All rights Rsrvd 6

7 Figur 5.2: Frqunt Pattrn Stram tr implmntation on snsor data. Aftr implmntation of th snsor data on Compact Frqunt pattrn stram tr with thrshold valu four and sliding window siz svn th rsult is shown in figur 5.3. Th first column in Figur 5.3 shows th class which is frqunt, th scond column shows th transaction in which th pattrn is frqunt and th thid column shows th support count of ach frqunt pattrn. Th thrshold is takn to b four and th sliding window siz is takn to b svn. Figur 5.3: Compact FP Stram Tr implmntation on snsor data. 5.3 Exprimntal Rsults Comparison of FP Stram tr and Compact FP Stram tr for tiltd tim window rquirmnt. Th Comparison tabl for th amount of th tiltd tim window to b maintaind at th nods of FP stram tr and Compact FP stram tr is shown in Tabl 5.1 Tabl: 5.1 Comparison FP stram tr and Compact FP stram tr basd on tiltd tim window Siz of th sliding window Numbr of th windows to b maintaind for FP stram tr Numbr of windows to b maintaind for Compact FP stram tr Prcntag rduction in numbr of windows in Compact FP stram tr All rights Rsrvd 7

8 rspct to FP stram tr % % % % % % Th window siz is varid and th thrshold is takn to b 10 in Tabl 5.1. Th abov rsult on snsor data shows that by incrasing th siz of th sliding window thr is rduction in th numbr of tiltd tim window to b maintaind in th Compact FP stram tr but th siz of th windows in FP stram tr rmains th sam bcaus th siz of th minimum numbr of windows ndd hav to b spcifid bfor th FP stram tr is constructd. Thus a compact structur is obtaind by Compact FP stram tr prcntag rduction in numbr of windows Siz of th sliding window Prcntag rduction in numbr of windows in Compact FP stram tr with rspct to FP stram tr 0 y-axis window siz x-axis Figur 5.4: Chart showing prcntag rduction in siz of tiltd tim window to b maintaind by Compact FP stram tr. Th graph givn in Figur 5.4 shows th prcntag rduction in amount of th tiltd tim window to b maintaind at nods of Compact FP stram tr with rspct to FP stram tr. Th graph is constructd by varying th window siz Comparison of spac rqirmnt for FP stram tr and Compact FP stram tr for th snsor basd data. Tabl 5.2 Spac rquirmnt by FP stram tr and Compact FP stram tr Window Siz of FP Stram Tr(byts) Siz of compact FP stram tr(byts) Prcntag Rduction in Siz % % % % % Th Tabl 5.2 shows th comparison of spac rquirmnt btwn FP stram tr and compact FP stram tr. Th comparison is carrid out by varying th window siz and kping th thrshold to b 10. Th abov tabl shows that with th incrasing th siz of th window th prformanc of Compact FP stram tr gradually incrass. Thus Compact FP stram tr rquirs lss spac as compard to FP stram All rights Rsrvd 8

9 prcntag rduction in spac of Compact FP stram tr compard to FP stram tr window siz prcntag rduction in tim of FP stram tr compard to FP stram tr y-axis Window siz x-axis Figur 5.5: Chart showing prcntag rduction in spac rquirmnt btwn FP stram tr and Compact FP stram tr. Th graph in Figur 5.5 shows th rduction in spac rquirmnt of Compact FP stram tr compard to FP stram tr. Th graph is constructd by varying window siz Comparison of tim rquirmnt by FP stram tr and Compact FP stram tr for th snsor basd data. Th Tabl 5.3 shows th comparison of tim rquirmnt btwn FP stram tr and Compact FP stram tr. Th comparison is carrid out by varying th window siz and kping th thrshold to b 10. Th abov tabl shows that with th incrasing th siz of t h window th prformanc of Compact FP stram tr gradually incrass. Thus Compact FP stram tr rquirs lss tim as compard to FP stram tr. Window Tabl 5.3 Tim rquirmnt by FP stram tr and Compact FP stram tr Tim rquirmnt for FP stram tr(in nanosconds) Tim rquimnt for compact FP stram tr(in nanosconds) Prcntag Rduction in Tim % % % % % Th Tabl 5.3 shows th comparison of tim rquirmnt btwn FP stram tr and Compact FP stram tr. Th comparison is carrid out by varying th window siz and kping th thrshold to b 10. Th abov tabl shows that with th incrasing th siz of th window th prformanc of Compact FP stram tr gradually incrass. Thus Compact FP stram tr rquirs lss tim as compard to FP stram tr. Th graph in Figur 5.6 shows th rduction in tim rquirmnt of Compact FP stram tr compard to FP stram tr. Th graph is constructd by varying window All rights Rsrvd 9

10 prcntag rduction in tim of Compact FP stram tr compard to FP stram tr y-axis Winow siz x-axis Figur 5.6: Chart showing prcntag rduction in tim rquirmnt btwn FP stram tr and Compact FP stram tr. 6. CONCLUSION Th proposd Compact FP Stram Tr maintains th tiltd tim windows at th tail nod and at th partition window. It uss linkd list basd tiltd tim window thus th amount of tiltd tim window to b maintaind at th nods nd not b spcifid in advanc. It maintains history of information ovr a priod of tim. Exprimntal rsults on Snsor data st shows that th amount of windows ndd to b maintaind in Compact FP stram tr is much lss as compard to FP stram tr. Th tim and spac rqirmnt of Compact FP stram tr is lss as compard to FP stram tr. Thus Compact FP stram finds frqunt pattrn ovr a priod of tim with compact data structur and in a tim fficint mannr. ACKNOWLEDGEMENT W would lik to thank rfrnc authors and also lik to thank th anonymous rviwrs, whos commnts and suggstions hav hlpd thm to improv th quality of th original manuscript. REFERENCES window siz prcntag rduction in tim of FP stram tr compard to FP stram tr 1. C. Giannlla; J. Han, J. Pi; X. Yan; P.S. Yu, Mining frqunt pattrns in data strams at multipl tim granularitis, in: Data Mining: Nxt Gnration Challngs and Futur Dirctions, AAAI/MIT Prss, 2004 (Chaptr 6). 2. S.K Tanbr; C.F. Ahmd; B. Jong, Y. L; Sliding window-basd frqunt pattrn mining ovr data strams, Information Scincs Elsvir, Vol. 179, Issu 22, pp , P. Zhang; X. Zhu; J. Tan; L. Guo, Classifir and Clustr Ensmbls for Mining Concpt Drifting Data Strams, IEEE Intrnational Confrnc on Data Mining, Sydny,13-17 Dc, C. Lin; D.Y Chiu; Y.H. Wu; A. Chn, Mining Frqunt Itmsts from Data Strams with a Tim- Snsitiv Sliding Window, In Proc. Socity of industrial and applid mathmatics(siam) Intrnational Confrnc on Data Mining,California,21-23 April, J. Gao; W. Fan; J. Han; P. S. Yu, A Gnral Framwork for Mining Concpt-Drifting Data Strams with Skwd Distributions, Socity for industrial and applid mathmatics(siam) Intrnational Confrnc on Data Mining, Minnapolis, April C. Li; K.F. Ja; R.P. Lin; S.F. Yn; C.W. Hsu, Mining frqunt pattrns from dynamic data strams with data load Managmnt, Journal of Systms and Softwar,vol 85,Issu 6, pp , M. kholghi; M. kyvanpour, An analytical framwork for Data stram mining tchniqus Basd on challngs and Rquirmnts, Intrnational journal of nginring scinc and tchnology (IJEST), vol. 3, no. 3, march C.K.-S. Lung; Q.I. Khan, DSTr: a tr structur for th mining of frqunt sts from data strams, in: Proc. ICDM, 2006, pp [12] F.-Y. Y, J.-D. Wang, B.-L. Shao, Nw algorithm for mining frqunt itmsts in spars databas, in: Proc. th Fourth Intrnational Confrnc on Machin Larning and Cybrntics, 2005, pp S.K. Tanbr, C.F. Ahmd, B.-S. Jong, Y.-K. L, CP-tr: a tr structur for singl-pass frqunt pattrn mining, in: T. Washio t al. (Eds.), Proc. PAKDD, 2008, pp All rights Rsrvd 10

11 10. P. Zhang, X. Zhu, Y. Shi, L. Guo, and X. Wu, Robust Ensmbl Larning for Mining Noisy Data Strams,Dcision Support Systms, Vol. 50, Issu 2, pp: , C.K.-S Lung; Q.I. Khan; Efficint mining of constraind frqunt pattrns from strams, in: Proc. 10th Intrnational Databas Enginring and Applications Symposium, X. Zhi-Jun, C. Hong, C. Li, An fficint algorithm for frqunt itmst mining on data strams, in: Proc. ICDM, 2006, pp G.K Gupta, Introduction to Data Mining with cas studis, PHI publication, 2nd dition, J. Han; M. kambr; J. Pi, Data mining concpts and tchniqus -Th Morgan Kaufmann sris in Data Managmnt Systms, 3 rd dition, V. pudi; P. R. Krishna, Data Mining, Oxford Univrsity Prss, 1 st dition, All rights Rsrvd 11