Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A Clustering Method for Nominal and Numerical Data Based on Rough Set Theory
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
A Clustering Method for Nominal and Numerical Data Based on Rough Set Theory
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
A rough set-based fuzzy clustering
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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This paper describes a new clustering method based on rough set theory. This method classifies objects according to the indiscernibility relations defined on the basis of relative similarity. First, an initial equivalence relation, which evaluates local similarity of objects, is assigned to every object. Then modification of the initial equivalence relations is performed by examining global relationships among them. An initial equivalence relation will be modified if it gives excessively fine classification to the objects. Consequently, generation of small category is suppressed and adequately coarse clusters are formed. Experimental results on the artificial data showed that this method produced good clustering results for both of nominal and numerical data.