Indiscernibility-based clustering: Rough clustering

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2715, Issue , 2003, Pages 378-386

  Hirano, Shoji ^a   Tsumoto, Shusaku ^a  

^a SHIMANE UNIVERSITY   (Japan)

Author keywords

Clustering; Indiscernibility; Rough sets

Indexed keywords

BINS; CLUSTERING ALGORITHMS; EQUIVALENCE CLASSES; FUZZY SYSTEMS; ITERATIVE METHODS; SET THEORY; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL GEOMETRY; FUZZY SETS;

BINARY CLASSIFICATION; CLUSTERING; CLUSTERING RESULTS; EQUIVALENCE RELATIONS; GEOMETRIC MEASURES; INDISCERNIBILITY; INDISCERNIBILITY DEGREE; ITERATIVE REFINEMENT;

ROUGH SET THEORY;

BINARY CLASSIFICATIONS; ITERATIVE REFINEMENT; RELATIVE PROXIMITY; ROUGH CLUSTERING;

EID: 7044273970     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-44967-1_45     Document Type: Conference Paper

References (4)

1. P. Berkhin (2002): Survey of Clustering Data Mining Techniques. Accrue Software Research Paper. URL: http://www.accrue.com/products/researchpapers.html.
2. Z. Pawlak (1991): Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht.
3. J. W. Grzymala-Busse and M. Noordeen (1988): "CRS- A Program for Clustering Based on Rough Set Theory, " Research report, Department of Computer Science, University of Kansas, TR-88-3, 13.
4. S. Hirano and S. Tsumoto (2003): An Indiscernibility-Based Clustering Method with Iterative Refinement of Equivalence Relations- Rough Clustering-, " Journal of Advanced Computational Intelligence and Intelligent Informatics, (in press).