A new cluster validity measure and its application to image compression

Pattern Analysis and Applications

Volumn 7, Issue 2, 2004, Pages 205-220

  Chou, C H ^a   Su, M C ^b   Lai, E ^c  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

^b NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^c TAMKANG UNIVERSITY   (Taiwan)

Author keywords

Cluster validity; Clustering algorithm; Image compression; Pattern recognition; Vector quantisation

Indexed keywords

EID: 4043077037     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-004-0218-1     Document Type: Article

References (45)

1. Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice Hall, Englewood Cliffs, NJ
2. Ḧppner F, Klawonn F, Kruse R, Runkler T (1999) Fuzzy cluster analysis-methods for classification, Data Analysis and Image Recognition. Wiley, Chichester
3. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York
4. Bezdek JC (1973) Fuzzy mathematics in pattern classification. PhD thesis, Cornell University
5. Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3(3):32-57
6. Gustafson DE, Kessel WC (1979) Fuzzy clustering with a fuzzy covariance matrix. In: Proceedings of the IEEE Conference on Decision and control, San Diego, January 1979, pp 761-766
7. Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Machine Intell 11:773-781
8. Dave RN (1989) Use of the adaptive fuzzy clustering algorithm to detect lines in digital images. In: DP Casasent (ed) Intelligent Robots and Computer Vision VIII, vol 1192, part 2, pp 600-611
9. Dave RN (1990) Fuzzy shell-clustering and application to circle detection in digital images. Int J Gen Syst 16:343-355
10. Man Y, Gath I (1994) Detection and separation of ring-shaped clusters using fuzzy clustering. IEEE Trans Pattern Anal Machine Intell 16:855-861
11. Dave RN, Bhaswan K (1992) Adaptive fuzzy c-shells clustering and detection of ellipses. IEEE Trans Neural Networks 3: 643-662
12. Höppner F (1997) Fuzzy shell clustering algorithms in image processing: fuzzy c-rectangular and 2-rectangular shells. IEEE Trans Fuzzy Syst 5:599-613
13. Su MC, Chou CH (1999) A competitive learning algorithm using symmetry. IEICE Trans Fund Electron Commun Comput Sci E82-A(4):680-687
14. Su MC, Chou CH (2001) A modified version of the k-means algorithm with a distance based on cluster symmetry. IEEE Trans Pattern Anal Machine Intell 23: 674-680
15. Karypis G, Han H, Kumar V (1999) Chameleon: hierarchical clustering using dynamic modeling. IEEE Comput 32:68-75
16. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering cluster in large spatial database with noise. In: Proceedings of the 2nd International Conference on Knowledge discovery and data mining. AAAI Press, Menlo Park, CA, pp 226-231
17. Guha S, Rastogi R, Shim K (1999) ROCK: a robust clustering algorithm for categorical attributes. In: Proceedings of the 15th International Conference on Data engineering. IEEE Computer Society Press, Los Alimitos, CA, pp 512-521
18. Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. In: Proceeding of the ACM SIGMOD International Conference on Management of data, pp 73-84
19. Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering for high dimensional data for data mining application. In: Proceedings of the ACM SIGMOD International Conference on Management of data, Seattle, WA, June 1998, pp 94-105
20. Ankerst M, Breunig M, Kriegel HP, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: Proceedings of the ACM SIGMOD International Conference on Management of data, Philadelphia, PA, June 1999. ACM Press, New York, pp 49-60
21. Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of data, Montreal, Canada, June 1996, pp 103-114
22. Huntsherger TL, Jacobs CL, Cannon RL (1985) Iterative fuzzy image segmentation. Pattern Recognition 18:131-138
23. Krishnapuram R, Frigui H, Nasraoui O (1995) Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation - I and II. IEEE Trans Fuzzy Syst 3:29-61
24. Jolion JM, Meer P, Bataouche S (1991) Robust clustering with applications in computer vision. IEEE Trans Pattern Anal Machine Intell 13:791-802
25. Frigui R, Krishnapuram R (1997) Clustering by competitive agglomeration. Pattern Recognition 30:1109-1119
26. Miyamoto S, Mukaidono M (1997) Fuzzy c-means as a regularization and maximum entropy approach. In: Proceedings of the 7th Fuzzy System Association World Confgress, Prague, Czech Republic, June 1997, vol 2, pp 86-92
27. Dunn JC (1974) Well separated clusters and optimal fuzzy partitions. J Cybernet 4:95-104
28. Bezdek JC (1974) Numerical taxonomy with fuzzy sets. J Math Biol 1:57-71
29. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Machine Intell 13:841-847
30. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Machine Intell 1:224-227
31. Milligan GW, Cooper MC (1985) An examination of procedures for determining the number of clusters in a data set. Psychimetrika 50:159-179
32. Jain AK, Murthy MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:265-323
33. Cheeseman P, Stutz J (1996) Bayesian classification (autoclass): theory and results. In: Fayyad UM, Piatesky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, CA
34. Heckerman D, Geiger D, Chickering DM (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 20:197-243
35. Hardy A (1996) On the number of clusters. Computat Statist Data Anal 23:83-96
36. Bandyopadhya S, Maulik U (2001) Nonparametric genetic clustering: comparison of validity indices. IEEE Trans Syst Man Cybernet Part C: Applic Rev 31:120-125
37. Bezdek JC, Pal NR (1998) Some new indexes of cluster validity. IEEE Trans Syst Man Cybernet 28: 301-315
38. Bensaid AM, Hall LO, Bezdek JC, Clarke CP, Silbiger ML, Arrington JA, Murtagh RF (1996) Validity-guided (Re) clustering with applications to image segmentation. IEEE Trans Fuzzy Syst 4:112-123
39. Dubes R, Jain AK (1979) Validity studies in clustering methodologies. Pattern Recognition 11:235-253
40. Linde Y, Buzo A, Gray R (1980) An algorithm for vector quantization design. IEEE Trans Commun 28:84-95
41. Gersho A, Ramamurthi B (1982) Image coding using vector quantization. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Paris, May 1982. IEEE, Piscataway, NJ, pp 428-431
42. Baker RL, Gray RM (1982) Image compression using non-adaptive spatial vector quantization. In: Proceedings of the 16th Asilomar Conference on Circuits, systems, and computers, pp 55-61
43. Baker RL (1984) Vector quantization of digital images. PhD Dissertation, Stanford University, Stanford, CA
44. Kim YK, Ra JB (1995) Adaptive learning method in self-organizing map for edge preserving vector quantization. IEEE Trans Neural Networks 6:278-280
45. Park DC, Woo YJ (2001) Weighted centroid neural network for edge preserving image compression. IEEE Trans Neural Networks 12:1134-1146