A Rough Set-Based Clustering Method with Modification of Equivalence Relations

Authors:
Shoji Hirano;Tomohiro Okuzaki;Yutaka Hata;Shusaku Tsumoto;Kouhei Tsumoto
Affiliations:
-;-;-;-;-
Venue:
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Year:
2001

Citing 2
Cited 1

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

Cluster-Based rough set construction

PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining

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Abstract

This paper presents a clustering method for nominal and numerical data based on rough set theory. We represent relative similarity between objects as a weighted sum of two types of distances: the Hamming distance for nominal data and the Mahalanobis distance for numerical data. On assigning initial equivalence relations to every object, modification of slightly different equivalence relations is performed to suppress excessive generation of categories. The optimal clustering result can be obtained by evaluating the cluster validity over all clusters generated with various values of similarity thresholds. After classification has been performed, features of each class are extracted based on the concept of value reduct. Experimental results on artificial data and amino acid data show that this method can deal well with both types of attributes.