An Indiscernibility-Based Clustering Method with Iterative Refinement of Equivalence Relations - Rough Clustering -

Journal of Advanced Computational Intelligence and Intelligent Informatics

Volumn 7, Issue 2, 2003, Pages 169-177

  Hirano, Shoji ^a   Tsumoto, Shusaku ^a  

^a SHIMANE UNIVERSITY   (Japan)

Author keywords

clustering; indiscernibility; rough sets

Indexed keywords

CLUSTERING ALGORITHMS; EQUIVALENCE CLASSES; ITERATIVE METHODS; SET THEORY;

BASED CLUSTERING; BINARY CLASSIFICATION; CLUSTERING METHODS; CLUSTERINGS; EQUIVALENCE RELATIONS; INDISCERNIBILITY; INDISCERNIBILITY DEGREE; ITERATIVE REFINEMENT; ROUGH CLUSTERING; ROUGH SET;

CLUSTER ANALYSIS;

EID: 3543125904     PISSN: 13430130     EISSN: 18838014     Source Type: Journal    
DOI: 10.20965/jaciii.2003.p0169     Document Type: Article

References (9)

1. B. S. Everitt, S. Landau, and M. Leese, Cluster Analysis Fourth Edition, Arnold Publishers, 20(11.
2. S. Z. Selim and M. A. Ismail, K-mcans-typc Algorithms, A Generalized Convergence Theorem and Characterization of Local Optimally. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(1): 81-87, 1984,
3. J. C, Bezdek (1981): Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum Press, New M)tk.
4. R. Agrawal, J. Gehrke, D, Gunopulos, and R Raghavan Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications., Proceedings of ACM S1GMOD International Conference on Management of Data: 94-105, 1998,
5. S. Guha, R. Rastogi, and K. Shim, CURE; An Efficient Clustering Algorithm for Large Databases., Proceedings of ACM SIGMOD International Conference on Management of Data; 73-84.
6. T Zhang, R. Ramakrishnan, and M. Livny, BIRCH: An Efficient Data Clustering Method for \ery Large Databases. Proceedings of ACM SIGMOD Internationa] Conference on Management of Data, 1(13-114, 1996.
7. Z. Pawlak, Rough Sets, Theoretical Aspects of Reasoning about Data., Kluwer Academic Publishers, dordrechl.
8. J. W Grzymala-Busse and M, Noordeen, CRS - A Program for Clustering Based on Rough Set Theory., Research report. Department of Computer Science, University of Kansas, TR-88-3, 13, 1988,
9. J. Neyman and E. L. Scott, Statistical Approach to Problems of Cosmology, Journal of the Royal Statistical Society, Series B20, 1-43, 14J5K.