Application of Clustering Algorithm in Big Data Sample Set Optimization
|
|
- Solomon Daniel
- 5 years ago
- Views:
Transcription
1 Applcaton of Clusterng Algorthm n Bg Data Sample Set Optmzaton Yutang Lu 1, Qn Zhang 2 1 Department of Basc Subjects, Henan Insttute of Technology, Xnxang , Chna 2 School of Mathematcs and Informaton Scence, Xnxang Unversty, Xnxang , Chna Abstract In order to solve the problem of poor clusterng accuracy and slow convergence speed of K-means clusterng algorthm n bg data envronment, A K-means algorthm based on optmzed samplng clusterng are proposed. The algorthm not only ensures the ratonalty of the ntal value of k-means algorthm but also makes the algorthm clusterng n a smaller sample set to mprove effcency. Fnally, the clusterng centers of the orgnal samples are obtaned by re-clusterng usng a bottom-up herarchcal clusterng method. The algorthm combnes the advantages of herarchcal method, partton method and densty method. The theoretcal analyss and expermental results show that the samplng clusterng algorthm has better clusterng accuracy than other algorthms, and has strong robustness and scalablty. Keywords: Bg data, K-means, Probablty samplng, Clusterng accuracy, Mult-cluster, Evdence theory. 1. INTRODUCTION As nformaton, technology contnues to evolve, many large enterprses, nsttutons and organzatons contnue to have access to a vast array of dverse and heterogeneous data, as well as the techncal ssues of effcently storng, processng and analyzng such valuable data. It s of great mportance to mne useful nformaton effcently from bg data sets (Jang, 2006). The clusterng method can reveal the ntrnsc relatonshp between data wthout pror knowledge, and can cluster valuable data of the same attrbute nto a sngle category, whch can excavate the valuable nformaton of campus network bg data for storage and further analyss of nformaton. However, the tme and space complexty of the tradtonal methods s nadequate under the campus network bg data (Bruno and For, 2013). The k-means algorthm mantans a lnear relatonshp wth the data sze n and satsfes the large-scale data processng However, there s an NP problem n the algorthm (Marchett and Zhou, 2014). It can be seen that the sze of the sample set and the degree of coverage of the orgnal large dataset category play a decsve role n mprovng the applcaton of the k-means algorthm n large dataset clusterng (Almaksour and Anquetl, 2014). 2. BIG DATA SAMPLING BASED ON LEADERS ALGORITHM 2.1 Leaders algorthm Leaders method based on the energy densty, by selectng each type of Leader pont to complete the clusterng, the algorthm s as follows. Leader algorthm does not need a pror to specfy the number of categores, and only needs to scan a large data set once to cluster (Chen and Lu, 2016). The advantages of Leader algorthm n dealng wth large data sets are obvous, but the algorthm s senstve to the nput sequence of data ponts. Appear wthn the class smlarty s greater than the smlartes between classes. 520
2 Clusterng result T1 Bg data set Improved collecton Clusterng result Tk Clustered successfully deleted data Clustered successfully deleted data New collecton Local clusterng results Fgure 1. Bg data set local ncremental clusterng method clusterng process 2.2 Samplng Whle mantanng the correlaton between the samples, the data ponts whose ntersectons n FIG. 1 are not correctly classfed are dstrbuted to dfferent sngle samples by random samplng Concentraton, so through a sngle sample set of clusterng to acheve ths part of the data category re-clusterng (Chen et al., 2014; Peddnt and Saxena, 2014). The amount of data for a sngle sample set. n 1 n 1 1 s f n f n n (1) 2 log( ) log( ) 2 log( ) n Accordng to the formula (1), the sze of the samplng sample set s calculated. In ths paper, m tmes of largescale data sets of sze n are randomly sampled for m tmes. The samplng condtons are as follows: C C (2) j n n (3) j n m n (4) In the formula, = 1, 2,..., m, j = 1,2,..., m, m Z and j, there s no ntersecton of each sample. 3. SAMPLE SET CLUSTERING CENTER CALCULATION METHOD At each samplng, the data n each ntal cluster center area s randomly sampled. In ths way, samplng can make the unon of sample sets reach the maxmum possble coverage of all categores n the orgnal data set, so that the category of samplng unon ts center s close to the orgnal bg data set. The expected samplng s that each sngle samplng set contans all the classes of the orgnal bg data set. 3.1 Determnaton of sngle sample cluster center Because of the small amount of data n a sngle sample set, a varety of classcal clusterng methods could be used for clusterng each sngle sample set. In ths paper, k-means algorthm s used to cluster each sngle sample set, n whch the ntal We choose the ntal cluster centers determned by the leaders preprocessng stage to solve the problem that the number of teratons of the algorthm s easly affected by the ntal cluster center settng (Langone et al., 2016). At the same tme, fast sample clusterng can be acheved due to the small amount of data n a sngle sample set. 521
3 Snce each sample set has the same sze, the sngle sample set clusterng process s performed ndependently, whch can acheve parallel processng and further reduce the processng tme of the algorthm. At the same tme, n order to synthesze the advantages of dfferent clusterng methods, when clusterng sngle sample sets, other algorthms such as K-medods can be used for dfferent sample sets to enhance the clusterng effect of sample sets. 3.2 Mult-sample cluster cluster fuson Suppose the number of categores n a bg data set s k, and the category covered by the th sample set s k (1 k k). After clusterng the sample set, n order to solve the stuaton shown n Fgure 1, some Natural clusters wll nevtably be subdvded. At the same tme, due to the naccuracy of the leaders clusterng centers, the number of clusters n some sample sets wll be greater than the ntal cluster centers, so the clusterng results of each sngle sample set need to be carred out Merge mergers to pnpont the orgnal bg data-clusterng center. Map: Generate samples randomly Reduce: wrte data to the correspondng sample Map: Fern sample gets cluster center Reduce: Consoldaton of all sample clusters Tradtonal parallel k- means clusterng bg data Probablty samplng Sample clusterng and result ntegraton Bg data clusterng Fgure 2. Sample Clusterng K-means Process When the dstance between two classes' mean s less than the preset threshold, the two classes are combned nto a sngle class and the mean of the new class s recalculated. 4. EXPERIMENTAL RESULTS AND ANALYSIS 4.1 Expermental envronment The expermental envronment cluster contans a total of 6 nodes, ncludng 1 master node and 5 slave nodes. Each node s connected va a 100Mb / s Fast Ethernet Swtch. Cluster conssts of 6 ordnary PC, each PC nstalled operatng system are Ubuntu14.04, memory sze s 2GB, hard drve capacty of 350GB. 4.2 Data set Two types of data sets are used n the experment: the frst s a Gaussan dstrbuton dataset, whch generates a 1-ggabyte Gaussan dstrbuton data set. The second s from a standard test set-real-world UCIrvne machne learnng lbrary Data Set Bag of Words Data Set (BoW), whch conssts of 3-D data ponts and represents 3 categores of features (doc ID, word ID, count). 4.3 Performance evaluaton In order to llustrate the robustness, scalablty and good clusterng performance of the algorthm, experments were carred out to compare the results of the two data sets. In ths experment, the parallel K-means, K-means n Map Reduce, K-means n Map Reduce, K-means n sample clusterng and K-means n optmzed sample clusterng were comparatvely analyzed. The expermental clusterng category k = 3, the number of samples s = 24, the number of data ponts for each sample accordng to the sze of the orgnal data set n the set V = {30000, 20000, 15000, 10000, 8000, 5000, 3000} value. 522
4 error/10 9 tme/10 3 s Revsta de la Facultad de Ingenería U.C.V., Vol. 32, N 14, pp , 2017 When the amount of data s small, the data n a stand-alone envronment only operates n memory and the calculaton speed s very fast. In a cluster envronment, a small amount of data occupes a large part of tme n the mport and export of varous compute nodes and dsks. Large amount of data, stand-alone memory s very dffcult to deal wth ths cluster wll show the advantages of dstrbuted computng, data transfer between nodes and dsk mport and export tme relatve to the entre task a small percentage of tme. Sample Samplng Clusterng K-means Gettng approxmate clusterng centers for large datasets wth representatve samples can dramatcally reduce the number of teratons requred for large data sets, reducng the amount of tme requred Data sze / MB Fgure 3. Tme performance curve Then compare the performance of each algorthm clusterng performance, as shown n Fgure 3. As can be seen from Fgure 3, the OSCK algorthm has the best clusterng performance and s obvously superor to the samplng clusterng K-means. Compared wth the clusterng K-means algorthm, the clusterng performance of the optmzed parallel K-means algorthm s better than that of the clusterng algorthm, but the clusterng performance s worse than that of the OSCK algorthm. By removng the sub-optmal clusterng centers of the samples, OSCK algorthm makes the fnal clusterng center more representatve, so t can mprove the clusterng accuracy to a certan extent Data sze / MB Fgure 4. Clusterng performance curve 523
5 Speedup rato tme/10 3 s Revsta de la Facultad de Ingenería U.C.V., Vol. 32, N 14, pp , 2017 Fnally, the algorthm s compared from the pont of vew of the nfluence of the number of nodes n the cluster on the performance of the algorthm. As can be seen from Fgure 5, the sample clusterng K-means algorthm and the OSCK algorthm are stll optmal n runtme when the number of nodes s changed. In addton, as the number of compute nodes ncreases, the runnng tme of each algorthm gradually decreases. However, Wll to some extent mprove performance. If T represents a sngle node calculaton tme, TP s a mult-node calculaton tme, the acceleraton rato Sp = T / TP Number of nodes Fgure 5. Algorthm tme-consumng contrast In order to verfy the robustness of the OSCK algorthm n processng large data sets, the experment uses Gaussan dstrbuton data set to run each algorthm 10 tmes, and statstcs the clusterng results of each run. The sample clusterng K-means and OSCK algorthms have good robustness compared wth the other two algorthms Number of nodes Fgure 6. Algorthm speedup comparson 5. CONCLUSIONS Wth the rapd development of network applcatons, computer networks have penetrated nto every area of socal lfe. Whle brngng strong mpetus to socal development, the ssue of network nformaton securty has 524
6 become an mportant ssue that affects the development of the Internet. Due to the dversty of connecton forms, termnal dstrbuton unevenness, network openness, and nterconnectvty, the network s vulnerable to attacks by hackers and other msdeeds. As t brngs great convenence and speed to people, but also brought huge rsks to people. Ths artcle focuses on the nformaton securty model, securty mechansms, and vrtual prvate network and ntruson detecton systems and so on. Among them, ntruson detecton system s the hot pont of network securty research n the future. The future technology wll be herarchcal and ntellgent development. Future IDSs should be able to detect and alert on ntruson at dfferent levels of the network protocol. REFERENCES Almaksour A., Anquetl E. (2014). Ilclass: error-drven antecedent learnng for evolvng takag-sugeno classfcaton systems, Appled Soft Computng, 19(2), Bruno G., For A. (2013). Mcroclan: mcroarray clusterng analyss, Journal of Parallel & Dstrbuted Computng, 73(3), Chen M.C., Kong X.S., Chen K. (2014). Applcaton of statstcal analyss software n food scentfc modelng, Advance Journal of Food Scence and Technology, 6(10), Chen M.C., Lu Q.L. (2016). Blow-up crtera of smooth solutons to a 3D model of electro-knetc fluds n a bounded doman, Electronc Journal of Dfferental Equatons, 128, 1-8. Jang W. (2006). Nonparametrc densty estmaton and clusterng n astronomcal sky surveys, Computatonal Statstcs & Data Analyss, 50(3), Langone R., Van B.M., Suykens J. (2016). Entropy-based ncomplete cholesky decomposton for a scalable spectral clusterng algorthm: computatonal studes and senstvty analyss, Entropy, 18(5), 182. Marchett Y., Zhou Q. (2014). Iteratve subsamplng n soluton path clusterng of nosy bg data, Statstcs, 9(4), 1-9. Peddnt S.T., Saxena N. (2014). Web search query prvacy: evaluatng query obfuscaton and anonymzng networks, Journal of Computer Securty, 22(1),
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 informationSubspace 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 informationDetermining the Optimal Bandwidth Based on Multi-criterion Fusion
Proceedngs of 01 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 5 (01) (01) IACSIT Press, Sngapore Determnng the Optmal Bandwdth Based on Mult-crteron Fuson Ha-L Lang 1+, Xan-Mn
More informationParallelism 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 informationNUMERICAL 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 informationProblem 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 informationNetwork Intrusion Detection Based on PSO-SVM
TELKOMNIKA Indonesan Journal of Electrcal Engneerng Vol.1, No., February 014, pp. 150 ~ 1508 DOI: http://dx.do.org/10.11591/telkomnka.v1.386 150 Network Intruson Detecton Based on PSO-SVM Changsheng Xang*
More informationAn Entropy-Based Approach to Integrated Information Needs Assessment
Dstrbuton Statement A: Approved for publc release; dstrbuton s unlmted. An Entropy-Based Approach to ntegrated nformaton Needs Assessment June 8, 2004 Wllam J. Farrell Lockheed Martn Advanced Technology
More informationHierarchical 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 informationContent 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 informationFast 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 informationBIN 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 informationResearch of Dynamic Access to Cloud Database Based on Improved Pheromone Algorithm
, pp.197-202 http://dx.do.org/10.14257/dta.2016.9.5.20 Research of Dynamc Access to Cloud Database Based on Improved Pheromone Algorthm Yongqang L 1 and Jn Pan 2 1 (Software Technology Vocatonal College,
More informationMachine Learning. Topic 6: Clustering
Machne Learnng Topc 6: lusterng lusterng Groupng data nto (hopefully useful) sets. Thngs on the left Thngs on the rght Applcatons of lusterng Hypothess Generaton lusters mght suggest natural groups. Hypothess
More informationBioTechnology. An Indian Journal FULL PAPER. Trade Science Inc.
[Type text] [Type text] [Type text] ISSN : 0974-74 Volume 0 Issue BoTechnology 04 An Indan Journal FULL PAPER BTAIJ 0() 04 [684-689] Revew on Chna s sports ndustry fnancng market based on market -orented
More informationTECHNIQUE 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 informationA new segmentation algorithm for medical volume image based on K-means clustering
Avalable onlne www.jocpr.com Journal of Chemcal and harmaceutcal Research, 2013, 5(12):113-117 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCRC5 A new segmentaton algorthm for medcal volume mage based
More informationCS 534: Computer Vision Model Fitting
CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust
More informationThe Shortest Path of Touring Lines given in the Plane
Send Orders for Reprnts to reprnts@benthamscence.ae 262 The Open Cybernetcs & Systemcs Journal, 2015, 9, 262-267 The Shortest Path of Tourng Lnes gven n the Plane Open Access Ljuan Wang 1,2, Dandan He
More informationPrivate Information Retrieval (PIR)
2 Levente Buttyán Problem formulaton Alce wants to obtan nformaton from a database, but she does not want the database to learn whch nformaton she wanted e.g., Alce s an nvestor queryng a stock-market
More informationPositive Semi-definite Programming Localization in Wireless Sensor Networks
Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer
More informationSmoothing Spline ANOVA for variable screening
Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory
More informationMaintaining 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 informationThe 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 informationJournal of Chemical and Pharmaceutical Research, 2014, 6(10): Research Article. Study on the original page oriented load balancing strategy
Avalable onlne www.jocpr.com Journal of hemcal and Pharmaceutcal Research, 2014, 6(10):274-280 Research Artcle IN : 0975-7384 ODEN(UA) : JPR5 tudy on the orgnal page orented load balancng strategy Kunpeng
More informationOutline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like:
Self-Organzng Maps (SOM) Turgay İBRİKÇİ, PhD. Outlne Introducton Structures of SOM SOM Archtecture Neghborhoods SOM Algorthm Examples Summary 1 2 Unsupervsed Hebban Learnng US Hebban Learnng, Cntd 3 A
More informationIncremental Learning with Support Vector Machines and Fuzzy Set Theory
The 25th Workshop on Combnatoral Mathematcs and Computaton Theory Incremental Learnng wth Support Vector Machnes and Fuzzy Set Theory Yu-Mng Chuang 1 and Cha-Hwa Ln 2* 1 Department of Computer Scence and
More informationAn Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc.
[Type text] [Type text] [Type text] ISSN : 97-735 Volume Issue 9 BoTechnology An Indan Journal FULL PAPER BTAIJ, (9), [333-3] Matlab mult-dmensonal model-based - 3 Chnese football assocaton super league
More informationLobachevsky 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 informationAngle-Independent 3D Reconstruction. Ji Zhang Mireille Boutin Daniel Aliaga
Angle-Independent 3D Reconstructon J Zhang Mrelle Boutn Danel Alaga Goal: Structure from Moton To reconstruct the 3D geometry of a scene from a set of pctures (e.g. a move of the scene pont reconstructon
More informationRemote Sensing Image Retrieval Algorithm based on MapReduce and Characteristic Information
Remote Sensng Image Retreval Algorthm based on MapReduce and Characterstc Informaton Zhang Meng 1, 1 Computer School, Wuhan Unversty Hube, Wuhan430097 Informaton Center, Wuhan Unversty Hube, Wuhan430097
More informationA 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 informationLearning 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 informationTsinghua 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 informationAn Image Fusion Approach Based on Segmentation Region
Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua
More informationSupport 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 informationParallelization of a Series of Extreme Learning Machine Algorithms Based on Spark
Parallelzaton of a Seres of Extreme Machne Algorthms Based on Spark Tantan Lu, Zhy Fang, Chen Zhao, Yngmn Zhou College of Computer Scence and Technology Jln Unversty, JLU Changchun, Chna e-mal: lutt1992x@sna.com
More informationCourse 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 informationOutline. Type of Machine Learning. Examples of Application. Unsupervised Learning
Outlne Artfcal Intellgence and ts applcatons Lecture 8 Unsupervsed Learnng Professor Danel Yeung danyeung@eee.org Dr. Patrck Chan patrckchan@eee.org South Chna Unversty of Technology, Chna Introducton
More informationA MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS
Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung
More informationUnsupervised Learning and Clustering
Unsupervsed Learnng and Clusterng Why consder unlabeled samples?. Collectng and labelng large set of samples s costly Gettng recorded speech s free, labelng s tme consumng 2. Classfer could be desgned
More informationConcurrent 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 informationAn 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 informationTHE PATH PLANNING ALGORITHM AND SIMULATION FOR MOBILE ROBOT
Journal of Theoretcal and Appled Informaton Technology 30 th Aprl 013. Vol. 50 No.3 005-013 JATIT & LLS. All rghts reserved. ISSN: 199-8645 www.jatt.org E-ISSN: 1817-3195 THE PATH PLANNING ALGORITHM AND
More informationELEC 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 informationAn Improved Image Segmentation Algorithm Based on the Otsu Method
3th ACIS Internatonal Conference on Software Engneerng, Artfcal Intellgence, Networkng arallel/dstrbuted Computng An Improved Image Segmentaton Algorthm Based on the Otsu Method Mengxng Huang, enjao Yu,
More informationA 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 informationFINDING 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 informationSkew Angle Estimation and Correction of Hand Written, Textual and Large areas of Non-Textual Document Images: A Novel Approach
Angle Estmaton and Correcton of Hand Wrtten, Textual and Large areas of Non-Textual Document Images: A Novel Approach D.R.Ramesh Babu Pyush M Kumat Mahesh D Dhannawat PES Insttute of Technology Research
More informationResearch and Application of Fingerprint Recognition Based on MATLAB
Send Orders for Reprnts to reprnts@benthamscence.ae The Open Automaton and Control Systems Journal, 205, 7, 07-07 Open Access Research and Applcaton of Fngerprnt Recognton Based on MATLAB Nng Lu* Department
More informationImprovement 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 informationClustering Algorithm of Similarity Segmentation based on Point Sorting
Internatonal onference on Logstcs Engneerng, Management and omputer Scence (LEMS 2015) lusterng Algorthm of Smlarty Segmentaton based on Pont Sortng Hanbng L, Yan Wang*, Lan Huang, Mngda L, Yng Sun, Hanyuan
More informationBRDPHHC: A Balance RDF Data Partitioning Algorithm based on Hybrid Hierarchical Clustering
015 IEEE 17th Internatonal Conference on Hgh Performance Computng and Communcatons (HPCC), 015 IEEE 7th Internatonal Symposum on Cyberspace Safety and Securty (CSS), and 015 IEEE 1th Internatonal Conf
More informationTESTING AND IMPROVING LOCAL ADAPTIVE IMPORTANCE SAMPLING IN LJF LOCAL-JT IN MULTIPLY SECTIONED BAYESIAN NETWORKS
TESTING AND IMPROVING LOCAL ADAPTIVE IMPORTANCE SAMPLING IN LJF LOCAL-JT IN MULTIPLY SECTIONED BAYESIAN NETWORKS Dan Wu 1 and Sona Bhatt 2 1 School of Computer Scence Unversty of Wndsor, Wndsor, Ontaro
More informationUnsupervised Learning
Pattern Recognton Lecture 8 Outlne Introducton Unsupervsed Learnng Parametrc VS Non-Parametrc Approach Mxture of Denstes Maxmum-Lkelhood Estmates Clusterng Prof. Danel Yeung School of Computer Scence and
More informationRisk-Based Packet Routing for Privacy and Compliance-Preserving SDN
Rsk-Based Packet Routng for Prvacy and Complance-Preservng SDN Karan K. Budhraja Abhshek Malvankar Mehd Bahram Chnmay Kundu Ashsh Kundu Mukesh Snghal, Unversty of Maryland, Baltmore County, MD, USA Emal:
More informationClassifier 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 informationFeature Reduction and Selection
Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components
More informationThe Study of Remote Sensing Image Classification Based on Support Vector Machine
Sensors & Transducers 03 by IFSA http://www.sensorsportal.com The Study of Remote Sensng Image Classfcaton Based on Support Vector Machne, ZHANG Jan-Hua Key Research Insttute of Yellow Rver Cvlzaton and
More informationModule 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 informationUnder-Sampling Approaches for Improving Prediction of the Minority Class in an Imbalanced Dataset
Under-Samplng Approaches for Improvng Predcton of the Mnorty Class n an Imbalanced Dataset Show-Jane Yen and Yue-Sh Lee Department of Computer Scence and Informaton Engneerng, Mng Chuan Unversty 5 The-Mng
More informationAn 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 informationPRÉSENTATIONS DE PROJETS
PRÉSENTATIONS DE PROJETS Rex Onlne (V. Atanasu) What s Rex? Rex s an onlne browser for collectons of wrtten documents [1]. Asde ths core functon t has however many other applcatons that make t nterestng
More informationA 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 informationThe Discriminate Analysis and Dimension Reduction Methods of High Dimension
Open Journal of Socal Scences, 015, 3, 7-13 Publshed Onlne March 015 n ScRes. http://www.scrp.org/journal/jss http://dx.do.org/10.436/jss.015.3300 The Dscrmnate Analyss and Dmenson Reducton Methods of
More informationKeyword-based Document Clustering
Keyword-based ocument lusterng Seung-Shk Kang School of omputer Scence Kookmn Unversty & AIrc hungnung-dong Songbuk-gu Seoul 36-72 Korea sskang@kookmn.ac.kr Abstract ocument clusterng s an aggregaton of
More informationChinese Word Segmentation based on the Improved Particle Swarm Optimization Neural Networks
Chnese Word Segmentaton based on the Improved Partcle Swarm Optmzaton Neural Networks Ja He Computatonal Intellgence Laboratory School of Computer Scence and Engneerng, UESTC Chengdu, Chna Department of
More informationA Novel Optimization Technique for Translation Retrieval in Networks Search Engines
A Novel Optmzaton Technque for Translaton Retreval n Networks Search Engnes Yanyan Zhang Zhengzhou Unversty of Industral Technology, Henan, Chna Abstract - Ths paper studes models of Translaton Retreval.e.
More informationScheduling 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 informationEfficient 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 informationPerformance 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 informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationSequential Projection Maximin Distance Sampling Method
APCOM & ISCM 11-14 th December, 2013, Sngapore Sequental Projecton Maxmn Dstance Samplng Method J. Jang 1, W. Lm 1, S. Cho 1, M. Lee 2, J. Na 3 and * T.H. Lee 1 1 Department of automotve engneerng, Hanyang
More informationCorner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity
Journal of Sgnal and Informaton Processng, 013, 4, 114-119 do:10.436/jsp.013.43b00 Publshed Onlne August 013 (http://www.scrp.org/journal/jsp) Corner-Based Image Algnment usng Pyramd Structure wth Gradent
More informationSimulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010
Smulaton: Solvng Dynamc Models ABE 5646 Week Chapter 2, Sprng 200 Week Descrpton Readng Materal Mar 5- Mar 9 Evaluatng [Crop] Models Comparng a model wth data - Graphcal, errors - Measures of agreement
More informationThree supervised learning methods on pen digits character recognition dataset
Three supervsed learnng methods on pen dgts character recognton dataset Chrs Flezach Department of Computer Scence and Engneerng Unversty of Calforna, San Dego San Dego, CA 92093 cflezac@cs.ucsd.edu Satoru
More informationFAHP and Modified GRA Based Network Selection in Heterogeneous Wireless Networks
2017 2nd Internatonal Semnar on Appled Physcs, Optoelectroncs and Photoncs (APOP 2017) ISBN: 978-1-60595-522-3 FAHP and Modfed GRA Based Network Selecton n Heterogeneous Wreless Networks Xaohan DU, Zhqng
More informationAnalysis of 3D Cracks in an Arbitrary Geometry with Weld Residual Stress
Analyss of 3D Cracks n an Arbtrary Geometry wth Weld Resdual Stress Greg Thorwald, Ph.D. Ted L. Anderson, Ph.D. Structural Relablty Technology, Boulder, CO Abstract Materals contanng flaws lke nclusons
More informationQuality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation
Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on
More informationResearch of Image Recognition Algorithm Based on Depth Learning
208 4th World Conference on Control, Electroncs and Computer Engneerng (WCCECE 208) Research of Image Recognton Algorthm Based on Depth Learnng Zhang Jan, J Xnhao Zhejang Busness College, Hangzhou, Chna,
More informationKeywords - Wep page classification; bag of words model; topic model; hierarchical classification; Support Vector Machines
(IJCSIS) Internatonal Journal of Computer Scence and Informaton Securty, Herarchcal Web Page Classfcaton Based on a Topc Model and Neghborng Pages Integraton Wongkot Srura Phayung Meesad Choochart Haruechayasak
More informationA Clustering Algorithm Solution to the Collaborative Filtering
Internatonal Journal of Scence Vol.4 No.8 017 ISSN: 1813-4890 A Clusterng Algorthm Soluton to the Collaboratve Flterng Yongl Yang 1, a, Fe Xue, b, Yongquan Ca 1, c Zhenhu Nng 1, d,* Hafeng Lu 3, e 1 Faculty
More informationJournal of Chemical and Pharmaceutical Research, 2014, 6(6): Research Article. A selective ensemble classification method on microarray data
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(6):2860-2866 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A selectve ensemble classfcaton method on mcroarray
More informationA 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 informationHelsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr)
Helsnk Unversty Of Technology, Systems Analyss Laboratory Mat-2.08 Independent research projects n appled mathematcs (3 cr) "! #$&% Antt Laukkanen 506 R ajlaukka@cc.hut.f 2 Introducton...3 2 Multattrbute
More informationApplication of Improved Fish Swarm Algorithm in Cloud Computing Resource Scheduling
, pp.40-45 http://dx.do.org/10.14257/astl.2017.143.08 Applcaton of Improved Fsh Swarm Algorthm n Cloud Computng Resource Schedulng Yu Lu, Fangtao Lu School of Informaton Engneerng, Chongqng Vocatonal Insttute
More informationAssignment # 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 informationA fast algorithm for color image segmentation
Unersty of Wollongong Research Onlne Faculty of Informatcs - Papers (Arche) Faculty of Engneerng and Informaton Scences 006 A fast algorthm for color mage segmentaton L. Dong Unersty of Wollongong, lju@uow.edu.au
More informationData Mining: Model Evaluation
Data Mnng: Model Evaluaton Aprl 16, 2013 1 Issues: Evaluatng Classfcaton Methods Accurac classfer accurac: predctng class label predctor accurac: guessng value of predcted attrbutes Speed tme to construct
More informationSolving 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 informationParallel matrix-vector multiplication
Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more
More informationFrom Comparing Clusterings to Combining Clusterings
Proceedngs of the Twenty-Thrd AAAI Conference on Artfcal Intellgence (008 From Comparng Clusterngs to Combnng Clusterngs Zhwu Lu and Yuxn Peng and Janguo Xao Insttute of Computer Scence and Technology,
More informationDeep 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 informationMachine 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 informationSensor Selection with Grey Correlation Analysis for Remaining Useful Life Evaluation
Sensor Selecton wth Grey Correlaton Analyss for Remanng Useful Lfe valuaton Peng Yu, Xu Yong, Lu Datong, Peng Xyuan Automatc est Control Insttute, Harbn Insttute of echnology, Harbn, 5, Chna pengyu@ht.edu.cn
More informationJournal of Chemical and Pharmaceutical Research, 2014, 6(6): Research Article
Avalable onlne www.jocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(6):2512-2520 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 Communty detecton model based on ncremental EM clusterng
More informationFace Recognition Method Based on Within-class Clustering SVM
Face Recognton Method Based on Wthn-class Clusterng SVM Yan Wu, Xao Yao and Yng Xa Department of Computer Scence and Engneerng Tong Unversty Shangha, Chna Abstract - A face recognton method based on Wthn-class
More informationTerm 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 informationA Statistical Model Selection Strategy Applied to Neural Networks
A Statstcal Model Selecton Strategy Appled to Neural Networks Joaquín Pzarro Elsa Guerrero Pedro L. Galndo joaqun.pzarro@uca.es elsa.guerrero@uca.es pedro.galndo@uca.es Dpto Lenguajes y Sstemas Informátcos
More informationAnalysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD
Analyss on the Workspace of Sx-degrees-of-freedom Industral Robot Based on AutoCAD Jn-quan L 1, Ru Zhang 1,a, Fang Cu 1, Q Guan 1 and Yang Zhang 1 1 School of Automaton, Bejng Unversty of Posts and Telecommuncatons,
More information