A proof of the convergence theorem of maximum-entropy clustering algorithm

Science in China, Series F: Information Sciences

Volumn 53, Issue 6, 2010, Pages 1151-1158

  Ren, Shi Jun ^a   Wang, Ya Dong ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Clustering algorithm; Convergence; Entropy; Fixed point

Indexed keywords

EID: 77953059755     PISSN: 10092757     EISSN: 18622836     Source Type: Journal    
DOI: 10.1007/s11432-010-3094-x     Document Type: Article

References (8)

1. Rose K, Gurewtiz E, Fox G. A deterministic annealing approach to clustering. Patt Recog Lett, 1990, 11: 589-594.
2. Yu J, Shi H, Huang H, et al. Counterexamples to convergence theorem of maximum-entropy clustering algorithm. Sci China Ser F-Inf Sci, 2003, 46: 321-326.
3. Zhang Z H, Zheng N, Shi G. Maximum-entropy clustering algorithm and its alobal convergence analysis. Sci China Ser E, 2001, 44: 89-101.
4. Karayiannis N B. MECA: Maximum entropy clustering algorithm. In: Proc IEEE Int Conf Fuzzy Syst, Orlando, FL, 1994. 630-635.
5. Li R P, Mukaidono M. A maximum entropy approach to fuzzy clustering. In: Proc of the 4th IEEE Intern Conf on Fuzzy Systems, Yokohama, Japan, 1995. 2227-2232.
6. Karayiannis N B. Fuzzy partition entropies and entropy constrained fuzzy clustering algorithms. J Intell Fuzzy Syst, 1997, 5: 103-111.
7. Miyamoto S, Mukaidono M. Fuzzy c-means as a regularization and maximum entropy approach. In: Proc of the 7th International Fuzzy Systems Association World Congress, Vol. II, June 25-30, Prague, Chech, 1997. 86-92.
8. http://www.sagemath.com.