Extensions of kmeans-type algorithms: A new clustering framework by integrating intracluster compactness and intercluster separation

IEEE Transactions on Neural Networks and Learning Systems

Volumn 25, Issue 8, 2014, Pages 1433-1446

  Huang, Xiaohui ^a   Ye, Yunming ^a   Zhang, Haijun ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Clustering; data mining; feature weighting; kmeans

Indexed keywords

DATA MINING; SEPARATION;

CLUSTERING; FEATURE WEIGHTING; INTER CLUSTERS; K-MEANS; MUTUAL INFORMATIONS; NEW CLUSTERING ALGORITHMS; OBJECTIVE FUNCTIONS; REAL LIFE DATASETS;

CLUSTERING ALGORITHMS;

EID: 84904754840     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2293795     Document Type: Article

References (37)

1. M. R. Anderberg, Cluster Analysis for Applications. New York, NY, USA: Academic, 1973.
2. M. Sardana and R. Agrawal, "A comparative study of clustering methods for relevant gene selection in microarray data," in Advances in Computer Science, Engineering & Applications. New York, NY, USA: Springer-Verlag, 2012, pp. 789-797.
3. Z. Deng, K. Choi, F. Chung, and S. Wang, "Enhanced soft subspace clustering integrating within-cluster and between-cluster information," Pattern Recognit., vol. 43, no. 3, pp. 767-781, 2010.
4. L. Jing, M. Ng, and J. Huang, "An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data," IEEE Trans. Knowl. Data Eng., vol. 19, no. 8, pp. 1026-1041, Aug. 2007. (Pubitemid 47000341)
5. L. Tang, H. Liu, and J. Zhang, "Identifying evolving groups in dynamic multi-mode networks," IEEE Trans. Knowl. Data Eng., vol. 24, no. 1, pp. 72-85, Jan. 2012.
6. J. Huang, M. Ng, H. Rong, and Z. Li, "Automated variable weighting in k-means type clustering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 5, pp. 657-668, May 2005. (Pubitemid 40608400)
7. J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. San Mateo, CA, USA: Morgan Kaufmann, 2011.
8. J. Bezdek, "A convergence theorem for the fuzzy ISODATA clustering algorithms," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-2, no. 1, pp. 1-8, Jan. 1980. (Pubitemid 10451076)
9. S. Selim and M. Ismail, "K-means-type algorithms: A generalized convergence theorem and characterization of local optimality," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-6, no. 1, pp. 81-87, Jan. 1984. (Pubitemid 14529037)
10. C. Aggarwal, J. Wolf, P. Yu, C. Procopiuc, and J. Park, "Fast algorithms for projected clustering," ACM SIGMOD Rec., vol. 28, no. 2, pp. 61-72, 1999. (Pubitemid 129597324)
11. C. Domeniconi, D. Papadopoulos, D. Gunopulos, and S. Ma, "Subspace clustering of high dimensional data," in Proc. SIAM Int. Conf. Data Mining, 2004, pp. 517-521.
12. M. Al-Razgan and C. Domeniconi, "Weighted clustering ensembles," in Proc. SIAM Int. Conf. Data Mining, 2006, pp. 258-269. (Pubitemid 43955542)
13. E. Chan, W. Ching, M. Ng, and J. Huang, "An optimization algorithm for clustering using weighted dissimilarity measures," Pattern Recognit., vol. 37, no. 5, pp. 943-952, 2004. (Pubitemid 38473837)
14. X. Chen, Y. Ye, X. Xu, and J. Zhexue Huang, "A feature group weighting method for subspace clustering of high-dimensional data," Pattern Recognit., vol. 45, no. 1, pp. 434-446, 2012.
15. X. Chen, X. Xu, J. Huang, and Y. Ye, "TW-k-means: Automated twolevel variable weighting clustering algorithm for multi-view data," IEEE Trans. Knowl. Data Eng., vol. 24, no. 4, pp. 932-944, Apr. 2013.
16. R. Fisher, "The use of multiple measurements in taxonomic problems," Ann. Human Genet., vol. 7, no. 2, pp. 179-188, 1936.
17. D. Cai, X. He, and J. Han, "Semi-supervised discriminant analysis," in Proc. IEEE 11th Int. Conf. Comput. Vis., Oct. 2007, pp. 1-7.
18. A. Jain, "Data clustering: 50 years beyond k-means," Pattern Recognit. Lett., vol. 31, no. 8, pp. 651-666, 2010.
19. R. Xu and D. Wunsch, "Survey of clustering algorithms," IEEE Trans. Neural Netw., vol. 16, no. 3, pp. 645-678, May 2005. (Pubitemid 40718010)
20. M. Steinbach, G. Karypis, and V. Kumar, "A comparison of document clustering techniques," in Proc. KDD Workshop Text Mining, vol. 400. 2000, pp. 525-526.
21. P. Bradley, U. Fayyad, and C. Reina, "Scaling clustering algorithms to large databases," in Proc. 4th Int. Conf. Knowl. Discovery Data Mining, 1998, pp. 9-15.
22. D. Arthur and S. Vassilvitskii, "K-means++: The advantages of careful seeding," in Proc. 18th Annu. ACM-SIAM Symp. Discrete Algorithms, 2007, pp. 1027-1035.
23. S. Shahnewaz, M. Rahman, and H. Mahmud, "A self acting initial seed selection algorithm for k-means clustering based on convex-hull," in Informatics Engineering and Information Science. New York, NY, USA: Springer-Verlag, 2011, pp. 641-650.
24. D. Pelleg and A. Moore, "X-means: Extending k-means with efficient estimation of the number of clusters," in Proc. 17th Int. Conf. Mach. Learn., San Francisco, CA, USA, 2000, pp. 727-734.
25. S. Das, A. Abraham, and A. Konar, "Automatic clustering using an improved differential evolution algorithm," IEEE Trans. Syst., Man Cybern., A, Syst. Humans, vol. 38, no. 1, pp. 218-237, Jan. 2008.
26. M. Yang, K. Wu, and J. Yu, "A novel fuzzy clustering algorithm," in Proc. IEEE Int. Symp. Comput. Intell. Robot. Autom., vol. 2. 2003, pp. 647-652.
27. K. Wu, J. Yu, and M. Yang, "A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests," Pattern Recognit. Lett., vol. 26, no. 5, pp. 639-652, 2005. (Pubitemid 40319628)
28. W. De Sarbo, J. Carroll, L. Clark, and P. Green, "Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables," Psychometrika, vol. 49, no. 1, pp. 57-78, 1984.
29. G. De Soete, "Optimal variable weighting for ultrametric and additive tree clustering," Qual. Quantity, vol. 20, no. 2, pp. 169-180, 1986.
30. G. De Soete, "OVWTRE: A program for optimal variable weighting for ultrametric and additive tree fitting," J. Classificat., vol. 5, no. 1, pp. 101-104, 1988.
31. V. Makarenkov and P. Legendre, "Optimal variable weighting for ultrametric and additive trees and k-means partitioning: Methods and software," J. Classificat., vol. 18, no. 2, pp. 245-271, 2001. (Pubitemid 33587487)
32. L. Parsons, E. Haque, and H. Liu, "Subspace clustering for high dimensional data: A review," ACM SIGKDD Explorat. Newslett., vol. 6, no. 1, pp. 90-105, 2004.
33. H. Frigui and O. Nasraoui, "Simultaneous clustering and dynamic keyword weighting for text documents," Survey of Text Mining. New York, NY, USA: Springer-Verlag, 2004, pp. 45-70.
34. A. Ahmad and L. Dey, "A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets," Pattern Recognit. Lett., vol. 32, no. 7, pp. 1062-1069, 2011.
35. C. Domeniconi, D. Gunopulos, S. Ma, B. Yan, M. Al-Razgan, and D. Papadopoulos, "Locally adaptive metrics for clustering high dimensional data," Data Mining Knowl. Discovery, vol. 14, no. 1, pp. 63-97, 2007. (Pubitemid 46345356)
36. J. Friedman and J. Meulman, "Clustering objects on subsets of attributes," J. R. Statist. Soc., Ser. B, vol. 66, no. 4, pp. 815-849, 2004.
37. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York, NY, USA: Wiley, 2012.