A time-efficient pattern reduction algorithm for k-means clustering

Information Sciences

Volumn 181, Issue 4, 2011, Pages 716-731

  Chiang, Ming Chao ^a   Tsai, Chun Wei ^a,b,c   Yang, Chu Sing ^b  

^a NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^c CHIA NAN UNIVERSITY OF PHARMACY AND SCIENCE   (Taiwan)

Author keywords

Data clustering; K means; Pattern reduction

Indexed keywords

COMPUTATION TIME; DATA CLUSTERING; EFFICIENT ALGORITHM; HIGH DIMENSIONAL DATA; ITERATION PATTERNS; K-MEANS; K-MEANS CLUSTERING; PATTERN REDUCTION; REDUCTION ALGORITHMS;

COMPUTATIONAL EFFICIENCY; DATA REDUCTION;

CLUSTERING ALGORITHMS;

EID: 78649849282     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2010.10.008     Document Type: Article

References (67)

1. R. Alcock, Synthetic control chart time series, 1999. Available from: .
2. R.M. Aliguliyev Performance evaluation of density-based clustering methods Information Sciences 179 20 2009 3583 3602
3. N Information Sciences 146 1-4 2002 221 237
4. S.D. Bay, KDD Cup 1998 data, 2000. Available from: .
5. M. Bereta, and T. Burczynski Immune k-means and negative selection algorithms for data analysis Information Sciences 179 10 2009 1407 1425
6. P.S. Bradley, U.M. Fayyad, Refining initial points for k-means clustering, in: Proceedings of the International Conference on Machine Learning, 1998, pp. 91-99.
7. P.S. Bradley, U.M. Fayyad, and C. Reina Scaling clustering algorithms to large databases Knowledge Discovery and Data Mining 1998 9 15
8. M.P.S. Brown, W.N. Grundy, D. Lin, N. Cristianini, C.W. Sugnet, T.S. Furey, M. Ares, D. Haussler, Knowledge-based analysis of microarry gene expression data by using support vector machines, in: Proceedings of the National Academy of Sciences of the United States of America, 97,1, 2000, pp. 262-267.
9. F. Camastra, and A. Verri A novel kernel method for clustering IEEE Transactions on Pattern Analysis and Machine Intelligence 27 5 2005 801 804
10. X. Cui, T.E. Potok, P. Palathingal, Document clustering using particle swarm optimization, in: Proceedings of the IEEE Swarm Intelligence Symposium, 2005, pp. 185-191.
11. W.H.E. Day Complexity theory: an introduction for practitioners of classification P. Arabie, L. Hubert, Clustering and Classification 1992 World Scientific Publishing Co., Inc. River Edge, NJ.
12. M.B. de Almeida, A. de Padua Braga, J.P. Braqa, Svm-km: speeding svms learning with a priori cluster selection and k-means, in: Proceedings of Sixth Brazilian Symposium on Neural Networks, 2000, pp. 162-167.
13. I.S. Dhillon, Y. Guan, B. Kulis, Kernel k-means, spectral clustering and normalized cuts, in: Proceedings of the Tenth ACM SIGKDD, 2004, pp. 551-556.
14. C. Elkan, Using the triangle inequality to accelerate k-means, in: Proceedings of the Twentieth International Conference on Machine Learning, 2003, pp. 147-153.
15. S. Eschrich, J. Ke, L.O. Hall, and D.B. Goldgof Fast accurate fuzzy clustering through data reduction IEEE Transactions on Fuzzy Systems 11 2 2003 262 270
16. M. Ester, H. Peter Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proceedings of International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226-231.
17. F. Farnstrom, J. Lewis, and C. Elkan Scalability for clustering algorithms revisited SIGKDD Explorations 2 1 2000 51 57
18. P. Ferragina, A. Gulli, A personalized search engine based on web-snippet hierarchical clustering, in: International World Wide Web Conference (WWW2005), 2005, pp. 801-810.
19. R.A. Fisher, Iris data set, 1936. Available from: .
20. T. Fromherz, P. Stucki, M. Bichsel, A survey of face recognition, MML Technical Report, 1997.
21. G. Getz, H. Gal, I. Kela, D.A. Notterman, and E. Domany Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data Bioinformatics 19 2003 12079 12084
22. A. Ghosh, A. Halder, M. Kothari, and S. Ghosh Aggregation pheromone density based data clustering Information Sciences 178 13 2008 2816 2831
23. F. Giannotti, M. Nanni, D. Pedreschi, F. Samaritani, Webcat: automatic categorization of web search results, in: Proceedings of SEBD'2003, 2003, pp. 507-518.
24. S. Guha, A. Meyerson, N. Mishra, and R. Motwani Clustering data streams: theory and practice IEEE Transactions on Knowledge and Data Engineering 15 3 2003 515 528
25. K.M. Hammouda, and M.S. Kamel Efficient phrase-based document indexing for web document clustering IEEE Transactions on Knowledge and Data Engineering 16 10 2004 1279 1296
26. C. Hua, Q. Chen, H. Wu, and T. Wada RK-means clustering: k-means with reliability IEICE Transactions on Information and Systems E91-D 1 2008 96 104
27. A.K. Jain, and R.C. Dubes Algorithms for Clustering Data 1988 Prentice Hall Englewood Cliffs, NJ
28. A.K. Jain, M.N. Murty, and P.J. Flynn Data clustering: a review ACM Computing Surveys 31 3 1999
29. M.F. Jiang, S.S. Tsent, and C.M. Su Two-phase clustering process for outliers detection Pattern Recognition Letters 22 6-7 2001 691 700
30. J. Kogan Introduction to Clustering Large and High-Dimensional Data 2007 Cambridge University Press NY, USA
31. K. Krishna, and M.N. Murty Genetic k-means algorithm IEEE Transactions on System, Man and Cybernetics - Part B:Cybernetics 29 3 1999 433 439
32. R.J. Kuo, L.M. Ho, and C.M. Hu Integration of self-organizing feature map and k-means algorithm for market segmentation International Journal of Computers and Operations Research 29 11 2002 1475 1493
33. R.J. Kuo, H.S. Wang, T.-L. Hu, and S.H. Chou Application of ant k-means on clustering analysis Computers and Mathematics with Applications 50 10-12 2005 1709 1724
34. M. Laszio, and S. Mukherjee A genetic algorithm using hyper-quadtrees for low-dimensional k-means clustering IEEE Transactions on Pattern Analysis and Machine Intelligence 28 4 2006 533 543
35. H. Lawrence Face recognition: a literature survey Review of Financial Studies 4 3 1991 389 415
36. C.-H. Lee, O.R. Zaïane, H.-H. Park, J. Huang, and R. Greiner Clustering high dimensional data: a graph-based relaxed optimization approach Information Sciences 178 23 2008 4501 4511
37. A. Leuski, Evaluating document clustering for interactive information retrieval, in: Proceedings of the Tenth International Conference on Information and Knowledge Management, 2001, pp. 33-40.
38. D.D. Lewis, Reuters-21578 text categorization collection, 1987. Available from: .
39. Y. Li, and S.M. Chung Parallel bisecting k-means with prediction clustering algorithm Journal of Supercomputing 39 1 2007 19 37
40. A. Likas, N. Vlassis, and J.J. Verbeek The global k-means clustering algorithm Pattern Recognition 36 2 2003 451 461
41. B. Liu Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) 2007 Springer
42. Y. Liu, Z. Yi, H. Wu, M. Ye, and K. Chen A tabu search approach for the minimum sum-of-squares clustering problem Information Sciences 178 12 2008 2680 2704
43. Y. Lu, S. Lu, F. Fotouhi, Y. Deng, S.J. Brown, Fgka: A fast genetic k-means clustering algorithm, in: Proceedings of Symposium on Applied Computing, 2004, pp. 622-623.
44. Y. Lu, S. Lu, F. Fotouhi, Y. Deng, and S.J. Brown Incremental genetic k-means algorithm and its application in gene expression data analysis International Journal of BMC Bioinformatics 5 172 2004
45. J.B. McQueen, Some methods of classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp. 281-297.
46. T. Mitchell, 20 newsgroups, 1999. Available from: .
47. M.K. Ng, and J.C. Wong Clustering categorical data sets using tabu search techniques Pattern Recognition 35 12 2002 2783 2790
48. R.T. Ng, and J. Han Clarans: a method for clustering objects for spatial data mining IEEE Transactions on Knowledge and Data Engineering 14 5 2002 1003 1016
49. C. Ordonez, and E. Omiecinski Efficient disk-based k-means clustering for relational databases IEEE Transactions on Knowledge and Data Engineering 16 8 2004 909 921
50. N.R. Pal, and J.C. Bezdek On cluster validity for the fuzzy c-means model IEEE Transactions on Fuzzy System 3 3 1995 370 379
51. D. Pelleg, A.W. Moore, X-means: extending k-means with efficient estimation of the number of clusters, in: Proceedings of the Seventeenth International Conference on Machine Learning, 2000, pp. 727-734.
52. B. Schölkopf, A. Smola, and K.-R. Müller Nonlinear component analysis as a kernel eigenvalue problem Neural Computation 10 1998 1299 1319
53. J. Shawe-Taylor, and N. Cristianini Kernel Methods for Pattern Analysis 2004 Cambridge University Press
54. M. Steinbach, G. Karypis,V. Kumar, A comparison of document clustering techniques, in: Sixth ACM SIGKDD International Conference on Data Mining (KDD'00), 2000, pp. 109-110.
55. M. Steinbach, G. Karypis, V. Kumar, A comparison of document clustering techniques, University of Minnesota Technical Report #00-034, 2000.
56. M.-C. Su, and H.-T. Chang Fast self-organizing feature map algorithm IEEE Transactions on Neural Networks 11 3 2000 721 733
57. C.-F. Tsai, Z.-C. Chen, C.-W. Tsai, MSGKA: an efficient clustering algorithm for large databases, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 5, 2002.
58. D. van der Merwe, A.P. Engelbrecht, Data clustering using particle swarm optimization, in: Proceedings of the 2003 Congress on Evolutionary Computation, 2003, pp. 215-220.
59. J. Vesanto, and E. Alhoniemi Clustering of the self-organizing map IEEE Transactions on Neural Networks 11 3 2000 586 600
60. R.W. Zhao, R. Chellappa, P.J. Philips, and A. Rosenfeld Face recognition: a literature survey ACM Computing Surveys (CSUR) 35 4 2003 399 458
61. W. Wang, J. Yang, R. Muntz, STING: a statistical information grid approach to spatial data mining, in: Proceedings of Conference on Very Large Databases, 1997, pp. 186-195.
62. R. Xu, and D. Wunsch II Survey of clustering algorithms IEEE Transaction on Neural Netowrks 16 3 2005 645 678
63. R. Xu, and D.C. Wunsch Clustering 2008 Wiley, John & Sons, Inc.
64. W. Xu, X. Liu, Y. Gong, Document clustering based on non-negative matrix factorization, in: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2003, pp. 267-273.
65. H.J. Zeng, Q.C. He, Z. Chen, W.Y. Ma, J. Ma, Learning to cluster web search results, in: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'04), 2004, pp. 210-217.
66. R. Zhang, A.I. Rudnicky, A large scale clustering scheme for kernel k-means, in: 16th International Conference on Pattern Recognition, 4, 2002, p. 40289.
67. T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for very large databases, in: Proceedings of ACM SIGMOD International Conference on Management of Data, 1996, pp. 103-114.