Clustering Algorithm Combining CPSO with K-Means Chunqin Gu 1, a, Qian Tao 2, b
|
|
- Magdalen Melton
- 5 years ago
- Views:
Transcription
1 Internatonal Conference on Advances n Mechancal Engneerng and Industral Informatcs (AMEII 05) Clusterng Algorthm Combnng CPSO wth K-Means Chunqn Gu, a, Qan Tao, b Department of Informaton Scence, Zhongka Unversty of Agrculture and Engneerng, Guangzhou, 505, Chna Department of Computer Scence, Guangdong Unversty of Educaton, Guangzhou, Chna a emal: guchunqn@gmal.com, b emal:tao66@39.com(correspondng author) Keywords: K-Means; Clusterng; Partcle Swarm Optmzaton; Chaotc Abstract. A clusterng algorthm combnng partcle swarm optmzaton (CPSO) wth K-Means (KM-CPSO) s proposed, whch features better search effcency than K-Means, PSO and CPSO. The K-Means algorthms cannot guarantee convergence to global optma and suffer n local optmal cluster center because they are senstve to ntal cluster centers. Chaotc partcle swarm optmzaton (CPSO) can fnd global optmal soluton; meanwhle K-Means can acheve local optma. The CPSO-KM algorthm utlzes both global search capablty of CPSO and local search capablty of K-Means. CPSO-KM algorthm has been tested wth two synthetc datasets and three classcal data sets from UCI. Expermental results show better performance of the CPSO-KM as compared to K-Means, PSO and CPSO. Introducton Clusterng s a common unsupervsed learnng method, whch parttons a group of obects (nstances) nto groups (clusters) such that obects n the same cluster are smlar to each other and dssmlar to the obects n other clusters. K-Means [] algorthm parttons the groups of gven obects nto k clusters based on a dstance metrc. The K-Means algorthm s easy to mplement and very effcent. The man drawback of the K-Means algorthm s that the clusterng result s senstve to the ntal clusters centers and may converge to the local optma []. In recent years, swarm ntellgent algorthms has been combned wth K-Means and appled on many clusterng problems, because of the ablty of global search of the swarm ntellgent algorthms, such as GA[3], PSO[4], ACO[5]. The combnaton of swarm ntellgent algorthms and K-Means algorthm can take advantage of both global search ablty of swarm ntellgent algorthms and local search ablty of K-Means algorthm. CPSO[6] has the advantage of more precse global search ablty and more fast convergence speed n our prevous lterature. The CPSO s used to obtan better clusters centers for ntal clusters centers, then K-Means algorthm s used based on the ntal clusters centers found by CPSO. The rest of the paper s organzed as follows. Secton descrbes the model of clusterng problems. Secton 3 presents our clusterng algorthm combnng CPSO wth K-Means (CPSO-KM). Secton 4 llustrates expermental results. Fnally, Secton 5 makes concluson. Model of clusterng problem The clusterng problem s a collecton of data obects that are smlar to one another wthn the same cluster and are dssmlar to the obects n other clusters. Gven a data obects set L DS {X,X,...,X N}, where X ( x,,..., ) x x, L s the dmenson of a data obect, a clusterng problem tres to fnd a K-partton of DS, C { C, C,..., C K }, such that the smlarty of the data obects n the same cluster s maxmum and the dfference of the data obects between dfferent cluster centrod s maxmum. The obectve functon of clusterng problem s evaluated based on the sum of squared error (SSE), whch s defned as 05. The authors - Publshed by Atlants Press 749
2 K mn D (x, C), C x x C C x C K () C DS, C C C, and, {,,..., K} where D(x, y) denotes the Eucldean dstance between x and y, the cluster C. C () (3) s the mean of data obects n Clusterng Algorthm Combnng CPSO wth K-Means In ths paper, we develop a hybrd algorthm for solvng clusterng problems, whose flowchart s shown n Fg.. It can be observed that CPSO s combned nto the clusterng algorthm. Detaled mplementatons of the algorthm are proposed and descrbed n ths secton. A. Partcle Swarm Optmzaton Partcle swarm optmzaton (PSO), whch was developed by Kennedy and Eberhart n 995[7,8], s a populaton-based swarm ntellgence algorthm. The PSO smulates the socal behavor of brds flockng and fsh schoolng. It has been successfully appled to many practcal optmzaton problems. In PSO, each partcle represents a canddate soluton n the soluton space of D D dmensons, whch has two vectors: a poston vector X [ x, x,..., x ] and a velocty D vectorv [ v, v,..., v ]. Durng the evolutonary process, the velocty vector and the poston vector of partcle on dmenson d at teraton t+ are updated as d d d d v ( t ) v () t c r ( pbest () t x ()) t (4) d d c r ( gbest ( t) x ( t)) d d d x ( t ) x ( t) v ( t ) (5) where d=,,,d represents each dmenson of the search space, s nerta weght, c and c are cogntve learnng coeffcent and socal learnng coeffcent, respectvely, r and r are two d unform random numbers n the range of [0, ], pbest () t s the poston on dmenson d wth the d best ftness found up to the tth teraton for the partcle, gbest () t s the best poston on dmenson d found by the whole partcle swarm. The nerta weght n (4) s usually updated as ( ) g g, max max mn max where and max are ntal and fnal weght, and set to 0.9 and 0.4, respectvely[9]. g s the mn current evolutonary generaton number, g s the maxmum number of generatons. max c and c are set.8, respectvely. Durng the evolutonary process, the velocty of each partcle on dmenson d d d s restrcted to the range of vmax, vmax, d vmax + d. The maxmum velocty vmax s usually set to 0% of the search range [0]. To fnd good ntal cluster center, the cluster centers should be encoded nto partcle s representaton. We employ a representaton that each partcle s characterzed by a D=Num_Attr*Num_Centrods dmensonal real number vector. Num_Attr s the number of attrbuton of nstances n the clusterng problems. Num_Centrods s the number of the centrods of nstances n the clusterng problems. Thus the partcle s encoded as a sequence of cluster centrods. The th partcle n the swarm s n the form of,,, Num _ Attr,,, Num _ Attr Num _ Centrods, Num _ Centrods, Num _ Centrods, Num _ Attr X [ x, x,..., x, x, x,..., x,..., x, x,..., x ] (6), k where x s the kth attrbuton value of the th centrod,,,..., Num_ Centrods, k,,..., Num_ Attr, Fg. shows the partcle representaton of the th partcle n a -D space. 750
3 Fg.. Partcle representaton n a -D space In the ntalzaton, the poston of all partcles are randomly generated, wth,,, Num_ Attr ( x, x,..., x ) s the attrbuton value of the th centrod(,,, K ). The velocty of each partcle s also randomly generated, whle the maxmum velocty of each dmenson s set to be 0% of the search range. B. K-Means The K-means algorthm s a straghtforward and wdely used clusterng algorthm. It s smple to mplement and run, relatvely fast. The K-Means algorthm parttons a gven dataset nto a user-specfed number of clusters. The standard K-Means algorthm s descrbed as follows: () Intalze the K cluster centrod M { m, m,..., m K } randomly. () Assgn each data pont x to the closest cluster based on Eucldean dstance. The dstance between data pont x to the closest cluster centrod mk s defned as Dx (, m) x m (7) k k (3) Recalculate the means of K cluster centers, and obtan new K centers. (4) Repeat step and 3 untl the average change n centrod vectors s less than a predefned value. C. Chaotc PSO algorthm(cpso) and ftness calculaton Otherwse, durng the evolutonary process, the PSO may trap nto a local optma soluton, then the exploraton performance wll not be mproved. CPSO ntroduces chaotc mappng wth certanty and stochastc property nto PSO n order to mprove the global convergence ablty []. The chaotc mappng method s the chaotc logstc sequence, whch s defned as follows: xt ( ) r x t x t, r N, x 0 [0,] (8) where r s the control parameter, y s a varable, r = 4, x 0,0.5,0.5,0.75, and t = 0,,,. Compute the radus of chaotc searchng regon to avod destroy the excellent soluton. The radus can be adaptvely adusted by the dstance between pbest and gbest and varance. The chaotc searchng tme s defned as ( pbest, gbest) ( pbest( partcle ) gbest ), PNum PNum d, (9) where s the threshold of the premature convergence. As descrbed n Secton II, the obectve of clusterng problem s the mnmzng of the SSE. 75
4 Fg.. Flowchart of CPSO-KM Experments and comparsons In ths secton, seven data sets are employed to valdate the proposed CPSO-KM clusterng algorthm and to compare the CPSO-KM wth conventonal K-Means, PSO and CPSO. A. Data sets and algorthm confguraton The fve data sets nclude three synthetc data sets (SynSet, SynSet) and three real data sets. The three synthetc data sets are generated based on normal dstrbuton wth mean vector and covarance matrx. The parameters for the three synthetc data sets are shown n TableⅠ. The synthetc data sets are relatvely smple, and the real number and centers poston of ther clusters are known n advance, so experments on these data sets can reveal the vrtues and defects of the algorthms. Fg. 3-4 graphcally presents the two synthetc data sets. Table Ⅰ. The parameters for two synthetc data sets Data Sets No. of No. of Sze of Parameters of clusters clusters dmensons clusters [0,0], 0.4 SynSet 3 50 [7,7], [3,3], [30, 30, 30], 4 [0, 0, 0], 4 SynSet [4,4,4], 3 4 [5,5,5], B 6 B A Fg. 3. Clusters of the SynSet Fg. 4. Clusters of the SynSet The three real data sets that we consdered are Irs, Haberman, Hayes-roth, whch are avalable 75-0 A
5 onlne []. These data sets are very classcal and often used to examne and compare the performances of algorthms n the felds of classfcaton. The PSO, CPSO and CPSO-KM use the same populaton sze of 0 and the same number of 000 ftness evaluatons (FEs) for a far comparson. All the experments are carred out usng Vsual C++ on the same machne wth a Pentum(R) Dual-Core CPU 3.0 GHz,.0GB RAM, and Wndows 7 operaton system. All the data sets are ndependently smulated 30 tmes, and ther mean results are compared n order to reduce the statstcal errors. B. Performance and F-Measure comparson between CPSO-KM and other algorthms To valdate the proposed CPSO-KM, we compared the CPSO-KM wth K-Means, PSO and CPSO. Each data set was ndependently smulated 30 tmes for purpose of reducng statstcal errors. The statstcal results are shown n Table Ⅱ n terms of the mean and standard devaton of the ftness, and boldface n the table ndcates the best result obtaned. Because the real parttons of the data sets consdered here are already known, the performances of CPSO-KM algorthm can be evaluated by comparng the resultng cluster wth the real structures n terms of external crtera. A commonly used crtera s F-Measure. Assumng that S s a prespecfed partton of data sets DS, C s the resultng clusters by applyng the CPSO-KM algorthm. () TP s the numbers of pars of data ponts ( x, x ), where x, x Cm, x, x Sn,. () FP s the numbers of pars of data ponts ( x, x ), where x, x Cm, x, Sn, x Sn,, n n. (3) TN s the numbers of pars of data ponts ( x, x ), where x Cm, x Cm, x Sn, x Sn,, m m, n n. (4) P and R are the precson and recall of cluster. (.0)( P R) F Measure, where TP ( P) R P, TP R TP FP TP FN In experments, we set.0, whch weghts precson and recall equally. The performance comparson between CPSO-KM and other algorthms n terms of F-Measure s llustrated n Table Ⅱ. An nterestng result s that all algorthms have most relably acheved the mnmal ftness and F-Measure of SysSet. It s shown that CPSO-KM attans better ftness than K-Means, PSO and CPSO. The CPSO-KM s also observed to obtan the smallest SD of the ftness and F-Measure. TABLE Ⅱ: Performance (ftness) and F-Measure comparson of CPSO-KM wth other algorthms Datasets K-Means PSO CPSO CPSO-KM SynSet (N=50, C=3, D=) SynSet (N=400, C=4, D=3) Irs (N=50, C=3, D=4) Haberman (N=306, C=, D=3) Hayes-roth (N=3, C=3, D=4) Mean ftness Std. Dev F-Measure Std. Dev Mean ftness.8643e e e e+04 Std. Dev F-Measure Std. Dev Mean ftness Std. Dev F-Measure Std. Dev Mean ftness E E E E+04 Std. Dev F-Measure Std. Dev Mean ftness Std. Dev F-Measure Std. Dev
6 Concluson In ths paper, a CPSO-KM algorthm s proposed to solve the clusterng problem, whch s based on combnaton of the partcle swarm optmzaton (PSO) and the K-Means algorthm. CPSO and K-Means are run sequentally and the cluster centers obtaned by CPSO are the ntal centers of the K-Means. The combnaton method solves the problem of senstvty to the choce of ntal cluster centers. The ftness experments on three synthetc and four real data sets show that the CPSO-KM algorthm can obtan better performance than K-Means, PSO and CPSO. The expermental results also show that CPSO-KM can attan the better clusters than other algorthms n terms of F-Measure. Future work ncludes research nto the applcaton of the CPSO-KM algorthm to solve more demandng data sets wth more complex structure, such as document clusterng. Acknowledgment Ths work s supported by the Postdoctoral Scence Foundaton of Chna (No. 03M549), Research Fund for the Doctoral Program of Hgher Educaton of Chna (No ), Guangdong Provnce Natural Scence Fund Proects (No. S ), Doctoral Foundaton of Guangdong Unversty of Educaton(No. 0ARF05), and Guangdong Provnce Colleage Specalty Constructon Proects (No. 03LYM_006), Guangdong Provncal Key Platform and Maor Scentfc Research Proects of Educaton(04KQNCX69, 04KTSCX94). References [] E. Forgy, Cluster analyss of multvarate data: Effcency vs. nter-pretablty of classfcatons, Bometrcs, ( 3), 965, [] S. Z. Selm, M. A. Ismal, K-means type algorthms: A generalzed convergence theorem and characterzaton of local optmalty, IEEE Transacton Pattern Analyss Machne Intellgence, 6, 984, 6, 8-87 [3] P. Sunantasaengtong, S. Chvapreecha, Mxed K-means and GA-based weghted dstance fngerprnt algorthm for ndoor localzaton system, n: Proceedngs of the 04 IEEE Regon 0 Conference TENCON, 04, -5 [4] M. Davod, H. R. Modares, E. Rehan, M. Davod, A. Sarkhan, Coherency approach by hybrd PSO, K-Means clusterng method n power system, n: Proceedngs of the IEEE nd Internatonal Power and Energy Conference, 008, [5] X. Sun, B. Zhang, L. Yang, S. L, L. Gao, Hyperspectal mage clusterng usng ant colony optmzaton(aco) mproved by K-means algorthm, n: Proceedngs of the 00 3rd Internatonal Conference on Advanced Computer Theory and Engneerng (ICACTE),, 00, V-474-V-478 [6] Q. Tao, Z. Huang, C. Gu et al., Optmzaton of green agr-food supply chan network usng chaotc PSO algorthm, n: Proceedngs of the 03 IEEE Internatonal Conference on Servce Operatons and Logstcs, and Informatcs (SOLI), 03, [7] J. Kennedy, R. C. Eberhart, Partcle swarm optmzaton, n: Proceedngs of the IEEE Internatonal Conference Neural Network, 995, 4, [8] R. C. Eberhart, J. Kennedy, A new optmzer usng partcle swarm theory, n: Proceedngs of the 6th Internatonal symposum on Mcromachne Human Scence, 995, [9] Y. Sh, R. C. Eberhart, A modfed partcle swarm optmzer, n Proceedngs IEEE World Congress Computer Intellgence, 998, pp [0] R. C. Eberhart, Y. H. Sh, Partcle swarm optmzaton: Developments, applcatons and resources, n: Proceedngs IEEE Congress Evolutonary Computaton, 00,
7 [] L. Zhang, Y. Zhang, Y. Yang. A Decorrelaton algorthm based on chaos adaptve. Journal of Electroncs & Informaton Technology, 009, 3(8), [] ftp://ftp.cs.uc.edu/pub/machne-learnng-databases/ 755
A Two-Stage Algorithm for Data Clustering
A Two-Stage Algorthm for Data Clusterng Abdolreza Hatamlou 1 and Salwan Abdullah 2 1 Islamc Azad Unversty, Khoy Branch, Iran 2 Data Mnng and Optmsaton Research Group, Center for Artfcal Intellgence Technology,
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 informationMeta-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 informationRecommended Items Rating Prediction based on RBF Neural Network Optimized by PSO Algorithm
Recommended Items Ratng Predcton based on RBF Neural Network Optmzed by PSO Algorthm Chengfang Tan, Cayn Wang, Yuln L and Xx Q Abstract In order to mtgate the data sparsty and cold-start problems of recommendaton
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 informationCluster 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 informationResearch of Neural Network Classifier Based on FCM and PSO for Breast Cancer Classification
Research of Neural Network Classfer Based on FCM and PSO for Breast Cancer Classfcaton Le Zhang 1, Ln Wang 1, Xujewen Wang 2, Keke Lu 2, and Ajth Abraham 3 1 Shandong Provncal Key Laboratory of Network
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 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 informationAn Improved Particle Swarm Optimization for Feature Selection
Journal of Bonc Engneerng 8 (20)?????? An Improved Partcle Swarm Optmzaton for Feature Selecton Yuannng Lu,2, Gang Wang,2, Hulng Chen,2, Hao Dong,2, Xaodong Zhu,2, Sujng Wang,2 Abstract. College of Computer
More informationCHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION
24 CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION The present chapter proposes an IPSO approach for multprocessor task schedulng problem wth two classfcatons, namely, statc ndependent tasks and
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 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 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 informationOptimal Design of Nonlinear Fuzzy Model by Means of Independent Fuzzy Scatter Partition
Optmal Desgn of onlnear Fuzzy Model by Means of Independent Fuzzy Scatter Partton Keon-Jun Park, Hyung-Kl Kang and Yong-Kab Km *, Department of Informaton and Communcaton Engneerng, Wonkwang Unversty,
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 informationClassifier Swarms for Human Detection in Infrared Imagery
Classfer Swarms for Human Detecton n Infrared Imagery Yur Owechko, Swarup Medasan, and Narayan Srnvasa HRL Laboratores, LLC 3011 Malbu Canyon Road, Malbu, CA 90265 {owechko, smedasan, nsrnvasa}@hrl.com
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 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 informationK-means Optimization Clustering Algorithm Based on Hybrid PSO/GA Optimization and CS validity index
Orgnal Artcle Prnt ISSN: 3-6379 Onlne ISSN: 3-595X DOI: 0.7354/jss/07/33 K-means Optmzaton Clusterng Algorthm Based on Hybrd PSO/GA Optmzaton and CS valdty ndex K Jahanbn *, F Rahmanan, H Rezae 3, Y Farhang
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 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 informationStudy of Data Stream Clustering Based on Bio-inspired Model
, pp.412-418 http://dx.do.org/10.14257/astl.2014.53.86 Study of Data Stream lusterng Based on Bo-nspred Model Yngme L, Mn L, Jngbo Shao, Gaoyang Wang ollege of omputer Scence and Informaton Engneerng,
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 informationUsing Particle Swarm Optimization for Enhancing the Hierarchical Cell Relay Routing Protocol
2012 Thrd Internatonal Conference on Networkng and Computng Usng Partcle Swarm Optmzaton for Enhancng the Herarchcal Cell Relay Routng Protocol Hung-Y Ch Department of Electrcal Engneerng Natonal Sun Yat-Sen
More informationHistogram based Evolutionary Dynamic Image Segmentation
Hstogram based Evolutonary Dynamc Image Segmentaton Amya Halder Computer Scence & Engneerng Department St. Thomas College of Engneerng & Technology Kolkata, Inda amya_halder@ndatmes.com Arndam Kar and
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 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 informationEVALUATION OF THE PERFORMANCES OF ARTIFICIAL BEE COLONY AND INVASIVE WEED OPTIMIZATION ALGORITHMS ON THE MODIFIED BENCHMARK FUNCTIONS
Academc Research Internatonal ISS-L: 3-9553, ISS: 3-9944 Vol., o. 3, May 0 EVALUATIO OF THE PERFORMACES OF ARTIFICIAL BEE COLOY AD IVASIVE WEED OPTIMIZATIO ALGORITHMS O THE MODIFIED BECHMARK FUCTIOS Dlay
More informationComplexity Analysis of Problem-Dimension Using PSO
Proceedngs of the 7th WSEAS Internatonal Conference on Evolutonary Computng, Cavtat, Croata, June -4, 6 (pp45-5) Complexty Analyss of Problem-Dmenson Usng PSO BUTHAINAH S. AL-KAZEMI AND SAMI J. HABIB,
More informationARTICLE IN PRESS. Applied Soft Computing xxx (2012) xxx xxx. Contents lists available at SciVerse ScienceDirect. Applied Soft Computing
ASOC-11; o. of Pages 1 Appled Soft Computng xxx (1) xxx xxx Contents lsts avalable at ScVerse ScenceDrect Appled Soft Computng j ourna l ho mepage: www.elsever.com/locate/asoc A herarchcal partcle swarm
More informationOptimizing SVR using Local Best PSO for Software Effort Estimation
Journal of Informaton Technology and Computer Scence Volume 1, Number 1, 2016, pp. 28 37 Journal Homepage: www.jtecs.ub.ac.d Optmzng SVR usng Local Best PSO for Software Effort Estmaton Dnda Novtasar 1,
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 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 informationComparison of Heuristics for Scheduling Independent Tasks on Heterogeneous Distributed Environments
Comparson of Heurstcs for Schedulng Independent Tasks on Heterogeneous Dstrbuted Envronments Hesam Izakan¹, Ath Abraham², Senor Member, IEEE, Václav Snášel³ ¹ Islamc Azad Unversty, Ramsar Branch, Ramsar,
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 informationAn Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation
17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed
More informationA hybrid sequential approach for data clustering using K-Means and particle swarm optimization algorithm
MultCraft Internatonal Journal of Engneerng, Scence and Technology Vol., No. 6, 00, pp. 67-76 INTERNATIONAL JOURNAL OF ENGINEERING, SCIENCE AND TECHNOLOGY www.jest-ng.com 00 MultCraft Lmted. All rghts
More informationA Deflected Grid-based Algorithm for Clustering Analysis
A Deflected Grd-based Algorthm for Clusterng Analyss NANCY P. LIN, CHUNG-I CHANG, HAO-EN CHUEH, HUNG-JEN CHEN, WEI-HUA HAO Department of Computer Scence and Informaton Engneerng Tamkang Unversty 5 Yng-chuan
More informationNatural Computing. Lecture 13: Particle swarm optimisation INFR /11/2010
Natural Computng Lecture 13: Partcle swarm optmsaton Mchael Herrmann mherrman@nf.ed.ac.uk phone: 0131 6 517177 Informatcs Forum 1.42 INFR09038 5/11/2010 Swarm ntellgence Collectve ntellgence: A super-organsm
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 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 informationSpeech enhancement is a challenging problem
Journal of Advances n Computer Engneerng and Technology, () 5 A New Shuffled Sub-swarm Partcle Swarm Optmzaton Algorthm for Speech Enhancement Masoud Geravanchzadeh, Sna Ghalam Osgoue Receved (-9-) Accepted
More informationHybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 16, No 2 Sofa 2016 Prnt ISSN: 1311-9702; Onlne ISSN: 1314-4081 DOI: 10.1515/cat-2016-0017 Hybrdzaton of Expectaton-Maxmzaton
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 informationA Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 A Tme-drven Data Placement Strategy for a Scentfc Workflow Combnng Edge Computng and Cloud Computng Bng Ln, Fangnng
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 informationStraight Line Detection Based on Particle Swarm Optimization
Sensors & ransducers 013 b IFSA http://www.sensorsportal.com Straght Lne Detecton Based on Partcle Swarm Optmzaton Shengzhou XU, Jun IE College of computer scence, South-Central Unverst for Natonaltes,
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 informationTraining ANFIS Structure with Modified PSO Algorithm
Proceedngs of the 5th Medterranean Conference on Control & Automaton, July 7-9, 007, Athens - Greece T4-003 Tranng ANFIS Structure wth Modfed PSO Algorthm V.Seyd Ghomsheh *, M. Alyar Shoorehdel **, M.
More informationNGPM -- A NSGA-II Program in Matlab
Verson 1.4 LIN Song Aerospace Structural Dynamcs Research Laboratory College of Astronautcs, Northwestern Polytechncal Unversty, Chna Emal: lsssswc@163.com 2011-07-26 Contents Contents... 1. Introducton...
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 informationA Notable Swarm Approach to Evolve Neural Network for Classification in Data Mining
A Notable Swarm Approach to Evolve Neural Network for Classfcaton n Data Mnng Satchdananda Dehur 1, Bjan Bhar Mshra 2 and Sung-Bae Cho 1 1 Soft Computng Laboratory, Department of Computer Scence, Yonse
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 informationFace Recognition University at Buffalo CSE666 Lecture Slides Resources:
Face Recognton Unversty at Buffalo CSE666 Lecture Sldes Resources: http://www.face-rec.org/algorthms/ Overvew of face recognton algorthms Correlaton - Pxel based correspondence between two face mages Structural
More informationParallel Artificial Bee Colony Algorithm for the Traveling Salesman Problem
Parallel Artfcal Bee Colony Algorthm for the Travelng Salesman Problem Kun Xu, Mngyan Jang, Dongfeng Yuan The School of Informaton Scence and Engneerng Shandong Unversty, Jnan, 250100, Chna E-mal: xukun_sdu@163.com,
More informationAnalysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment
Analyss of Partcle Swarm Optmzaton and Genetc Algorthm based on Tas Schedulng n Cloud Computng Envronment Frederc Nzanywayngoma School of Computer and Communcaton Engneerng Unversty of Scence and Technology
More informationBOOSTING CLASSIFICATION ACCURACY WITH SAMPLES CHOSEN FROM A VALIDATION SET
1 BOOSTING CLASSIFICATION ACCURACY WITH SAMPLES CHOSEN FROM A VALIDATION SET TZU-CHENG CHUANG School of Electrcal and Computer Engneerng, Purdue Unversty, West Lafayette, Indana 47907 SAUL B. GELFAND School
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 informationCombining Cellular Automata and Particle Swarm Optimization for Edge Detection
Combnng Cellular Automata and Partcle Swarm Optmzaton for Edge Detecton Safa Djemame Ferhat Abbes Unversty Sétf, Algera Mohamed Batouche Mentour Unversty Constantne, Algera ABSTRACT Cellular Automata can
More informationA high precision collaborative vision measurement of gear chamfering profile
Internatonal Conference on Advances n Mechancal Engneerng and Industral Informatcs (AMEII 05) A hgh precson collaboratve vson measurement of gear chamferng profle Conglng Zhou, a, Zengpu Xu, b, Chunmng
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 informationMaximum Variance Combined with Adaptive Genetic Algorithm for Infrared Image Segmentation
Internatonal Conference on Logstcs Engneerng, Management and Computer Scence (LEMCS 5) Maxmum Varance Combned wth Adaptve Genetc Algorthm for Infrared Image Segmentaton Huxuan Fu College of Automaton Harbn
More informationClassifier Ensemble Design using Artificial Bee Colony based Feature Selection
IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 3, No 2, May 2012 ISSN (Onlne): 1694-0814 www.ijcsi.org 522 Classfer Ensemble Desgn usng Artfcal Bee Colony based Feature Selecton Shunmugaprya
More informationAPPLICATION OF IMPROVED K-MEANS ALGORITHM IN THE DELIVERY LOCATION
An Open Access, Onlne Internatonal Journal Avalable at http://www.cbtech.org/pms.htm 2016 Vol. 6 (2) Aprl-June, pp. 11-17/Sh Research Artcle APPLICATION OF IMPROVED K-MEANS ALGORITHM IN THE DELIVERY LOCATION
More informationData Mining For Multi-Criteria Energy Predictions
Data Mnng For Mult-Crtera Energy Predctons Kashf Gll and Denns Moon Abstract We present a data mnng technque for mult-crtera predctons of wnd energy. A mult-crtera (MC) evolutonary computng method has
More informationA Self-adaptive Similarity-based Fitness Approximation for Evolutionary Optimization
A Self-adaptve Smlarty-based Ftness Approxmaton for Evolutonary Optmzaton Je Tan Dvson of Industral and System Engneerng, Tayuan Unversty of Scence and Technology, Tayuan, 34 Chna College of Informaton
More informationA New Hybrid Method Based on Improved Particle Swarm Optimization, Ant Colony Algorithm and HMM for Web Information Extraction
A New Hybrd Method Based on Improved Partcle Swarm Optmzaton, Ant Colony Algorthm and HMM for Web Informaton Extracton Rong LI, Hong-bn WANG Department of Computer, Xnzhou Teachers Unversty, Xnzhou, Chna
More informationPredator-Prey Pigeon-Inspired Optimization for UAV Three-Dimensional Path Planning
Predator-Prey Pgeon-Inspred Optmzaton for UAV Three-Dmensonal Path Plannng Bo Zhang 1 and Habn Duan 1,2,* 1 Scence and Technology on Arcraft Control Laboratory, School of Automaton Scence and Electrcal
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 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 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 informationProgramming 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 informationOn Supporting Identification in a Hand-Based Biometric Framework
On Supportng Identfcaton n a Hand-Based Bometrc Framework Pe-Fang Guo 1, Prabr Bhattacharya 2, and Nawwaf Kharma 1 1 Electrcal & Computer Engneerng, Concorda Unversty, 1455 de Masonneuve Blvd., Montreal,
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 informationMULTISPECTRAL 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 informationParameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis
Parameters Optmzaton of SVM Based on Improved FOA and Its Applcaton n Fault Dagnoss Qantu Zhang1*, Lqng Fang1, Sca Su, Yan Lv1 1 Frst Department, Mechancal Engneerng College, Shjazhuang, Hebe Provnce,
More informationUSING MODIFIED FUZZY PARTICLE SWARM OPTIMIZATION ALGORITHM FOR PARAMETER ESTIMATION OF SURGE ARRESTERS MODELS
Internatonal Journal of Innovatve Computng, Informaton and Control ICIC Internatonal c 2012 ISSN 1349-4198 Volume 8, Number 1(B), January 2012 pp. 567 581 USING MODIFIED FUZZY PARTICLE SWARM OPTIMIZATION
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 informationAvailable 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 informationK-means and Hierarchical Clustering
Note to other teachers and users of these sldes. Andrew would be delghted f you found ths source materal useful n gvng your own lectures. Feel free to use these sldes verbatm, or to modfy them to ft your
More informationA parallel implementation of particle swarm optimization using digital pheromones
Mechancal Engneerng Conference Presentatons, Papers, and Proceedngs Mechancal Engneerng 006 A parallel mplementaton of partcle swarm optmzaton usng dgtal pheromones Vjay Kalvarapu Iowa State Unversty,
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 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 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 informationAn Evolvable Clustering Based Algorithm to Learn Distance Function for Supervised Environment
IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, Issue 5, September 2010 ISSN (Onlne): 1694-0814 www.ijcsi.org 374 An Evolvable Clusterng Based Algorthm to Learn Dstance Functon for Supervsed
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 informationA 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 informationAn Influence of the Noise on the Imaging Algorithm in the Electrical Impedance Tomography *
Open Journal of Bophyscs, 3, 3, 7- http://dx.do.org/.436/ojbphy.3.347 Publshed Onlne October 3 (http://www.scrp.org/journal/ojbphy) An Influence of the Nose on the Imagng Algorthm n the Electrcal Impedance
More informationLoad 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 informationA Clustering Algorithm for Key Frame Extraction Based on Density Peak
Journal of Computer and Communcatons, 2018, 6, 118-128 http://www.scrp.org/ournal/cc ISSN Onlne: 2327-5227 ISSN Prnt: 2327-5219 A Clusterng Algorthm for Key Frame Extracton Based on Densty Peak Hong Zhao
More informationRobust Mean Shift Tracking with Corrected Background-Weighted Histogram
Robust Mean Shft Trackng wth Corrected Background-Weghted Hstogram Jfeng Nng, Le Zhang, Davd Zhang and Chengke Wu Abstract: The background-weghted hstogram (BWH) algorthm proposed n [] attempts to reduce
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 informationA Novel Deluge Swarm Algorithm for Optimization Problems
A Novel eluge Swarm Algorthm for Optmzaton Problems Anahta Samad,* - Mohammad Reza Meybod Scence and Research Branch, Islamc Azad Unversty, Qazvn, Iran Soft Computng Laboratory, Computer Engneerng and
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 informationCollaboratively Regularized Nearest Points for Set Based Recognition
Academc Center for Computng and Meda Studes, Kyoto Unversty Collaboratvely Regularzed Nearest Ponts for Set Based Recognton Yang Wu, Mchhko Mnoh, Masayuk Mukunok Kyoto Unversty 9/1/013 BMVC 013 @ Brstol,
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 informationMachine Learning 9. week
Machne Learnng 9. week Mappng Concept Radal Bass Functons (RBF) RBF Networks 1 Mappng It s probably the best scenaro for the classfcaton of two dataset s to separate them lnearly. As you see n the below
More informationA Weighted Method to Improve the Centroid-based Classifier
016 Internatonal onference on Electrcal Engneerng and utomaton (IEE 016) ISN: 978-1-60595-407-3 Weghted ethod to Improve the entrod-based lassfer huan LIU, Wen-yong WNG *, Guang-hu TU, Nan-nan LIU and
More informationUser 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