A multi-prototype clustering algorithm

Pattern Recognition

Volumn 42, Issue 5, 2009, Pages 689-698

  Liu, Manhua ^a   Jiang, Xudong ^b   Kot, Alex C ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Cluster prototype; Data clustering; Separation measure; Squared error clustering

Indexed keywords

COMPUTATIONAL METHODS; CONTENT BASED RETRIEVAL; DATA STRUCTURES; DIGITAL SIGNAL PROCESSING; FILE ORGANIZATION; LEARNING ALGORITHMS; SEPARATION;

AGGLOMERATIVE METHODS; ARBITRARY SHAPES; CLUSTER PROTOTYPE; CLUSTER SEPARATIONS; CLUSTERING PERFORMANCES; COMPLEX DATA STRUCTURES; COMPLEX STRUCTURES; COMPUTATIONAL COSTS; DATA CLUSTERING; DATA SETS; HIGH DENSITIES; INHERENT STRUCTURES; NUMBER OF CLUSTERS; SQUARED-ERROR CLUSTERING; SYNTHETIC AND REAL DATUMS;

CLUSTERING ALGORITHMS;

EID: 58249093822     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.09.015     Document Type: Article

References (35)

1. Jain A.K., and Dubes R.C. Algorithms for Clustering Data (1988), Prentice-Hall, Englewood Cliffs, NJ
2. Okabe A., Boots B., Sugihara K., and Chui S.N. Spatial Tessellations, Concepts and Applications of Voronoi Diagrams (2000), Wiley, New York
3. H. Koivistoinen, M. Ruuska, T. Elomaa, A Voronoi diagram approach to autonomous clustering, discovery science, Proceedings of the 9th International Conference, Springer, Berlin, 2006, pp. 149-160.
4. C. Reyes, M. Adjouadi, A clustering technique for random data classification, IEEE International Conference on Systems, Man and Cybernetics, vol.1, October 1995, pp. 316-321.
5. J. Li, P. Hao, Hierarchical structuring of data on manifolds, IEEE Conference on Computer Vision and Pattern Recognition, June 2007, pp. 1-8.
6. Jain A.K., Murthy M., and Flynn P. Data clustering: a review. ACM Comput. Surveys 31 3 (1999) 264-323
7. Sneath P.H.A., and Sokal R.R. Numerical Taxonomy (1973), Freeman, San Francisco, London
8. King B. Step-wise clustering procedures. J. Am. Statist. Assoc. 69 (1967) 86-101
9. S. Guha, R. Rastogi, K. Shim, Cure: an efficient clustering algorithm for large databases, Proceedings of the Conference on Management of Data (ACM SIGMOD), 1998, pp. 73-84.
10. Karypis G., Han E.-H.S., and Kumar V. Chameleon: a hierarchical clustering algorithm using dynamic modeling. IEEE Comput. 32 8 (1999) 68-75
11. Fred A.L., and Leitao J.M. A new cluster isolation criterion based on dissimilarity increments. IEEE Trans. Pattern Anal. Mach. Intell. 25 8 (2003) 944-958
12. T. Zhang, R. Ramakrishnan, M. Livny, Birch: an efficient data clustering method for very large databases, Proceedings of the Conference on Management of Data (ACM SIGMOD), 1996, pp. 103-114.
13. Lin C.-R., and Chen M.-S. Combining partitional and hierarchical algorithms for robust and efficient data clustering with cohesion self-merging. IEEE Trans. Knowl. Data Eng. 17 2 (2005) 145-159
14. Murty M.N., and Krishan G. A hybrid clustering procedure for concentric and chain-like clusters. Int. J. Comput. Inform. Sci. 10 6 (1981) 397-412
15. E.Y. Cheu, Z.Z. Chee Keong Kwoh, On the two-level hybrid clustering algorithm, The International Conference on Artificial Intelligence in Science and Technology (AISAT), 2004, pp. 138-142.
16. Topchy A., Jain A.K., and Punch W. Clustering ensembles: models of consensus and weak partitions. IEEE Trans. Pattern Anal. Mach. Intell. 27 12 (2005) 1866-1881
17. Kothari R., and Pitts D. On finding the number of clusters. Pattern Recognition Lett. 20 (1999) 405-416
18. Chakravarthy S.V., and Ghosh J. Scale-based clustering using the radial basis function network. IEEE Trans. Neural Networks 7 (1996) 1250-1261
19. A. Ben-Hur, D. Horn, H.T. Siegelmann, V. Vapnik, Support vector clustering, J. Mach. Learn. Res. (2) (2001) 125-137.
20. G. McLachlan K. Basford, Mixture Models: Inference and Application to Clustering, Marcel Dekker, New York, 1988.
21. Figueiredo M., and Jain A. Unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 24 3 (2002) 381-396
22. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD), 1996, pp. 226-231.
23. Shi Y., Song Y., and Zhang A. A shrinking-based clustering approach for multidimensional data. IEEE Trans. Knowl. Data Eng. 17 10 (2005) 1389-1403
24. M. Ankerst M.M. Breunig H.-P. Kriegel, J. Sander, Optics: ordering points to identify the clustering structure, Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 1999, pp. 49-60.
25. McQueen J. Some methods for classification and analysis of multivariate observations. Fifth Berkeley Symposium on Mathematical Statistics and Probability 1 (1967) 281-297
26. Sarkar M., Yegnanarayana B., and Khemani D. A clustering algorithm using an evolutionary programming-based approach. Pattern Recognition Lett. 18 (1997) 975-986
27. Khan S.S., and Ahmad A. Cluster center initialization algorithm for k-means clustering. Pattern Recognition Lett. 25 (2004) 1293-1302
28. Su M.-C., and Chou C.-H. A modified version of the k-means algorithm with a distance based on cluster symmetry. IEEE Trans. Pattern Anal. Mach. Intell. 23 6 (2001) 674-680
29. Charalampidis D. A modified k-means algorithm for circular invariant clustering. IEEE Trans. Pattern Anal. Mach. Intell. 27 12 (2005) 1856-1865
30. G.H. Ball, D.J. Hall, Isodata, a novel method of data analysis and classification, Technique Report, Stanford University, 1965.
31. Veenman C.J., Reinders M.J., and Backer E. A maximum variance clustering algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 24 9 (2002) 1273-1280
32. Davies D., and Bouldin D. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1 4 (1979) 224-227
33. Tyree E.W., and Long J.A. The use of linked line segments for cluster representation and data reduction. Pattern Recognition Lett. 20 1 (1999) 21-29
34. Su M.-C., and Liu Y.-C. A new approach to clustering data with arbitrary shapes. Pattern Recognition 38 11 (2005) 1887-1901
35. C.J. Merz, P.M. Murphy, Uci repository of machine learning databases, Department of Information and Computer Science, University of California 〈http://www.ics.uci.edu/mlearn/MLRepository.html〉.