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A Rough Set Based Clustering Method by Knowledge Combination
Tomohiro OKUZAKI Shoji HIRANO Syoji KOBASHI Yutaka HATA Yutaka TAKAHASHI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2002/12/01
Print ISSN: 0916-8532
Type of Manuscript: PAPER
rough set, clustering, knowledge combination,
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This paper presents a rough sets-based method for clustering nominal and numerical data. This clustering result is independent of a sequence of handling object because this method lies its basis on a concept of classification of objects. This method defines knowledge as sets that contain similar or dissimilar objects to every object. A number of knowledge are defined for a data set. Combining similar knowledge yields a new set of knowledge as a clustering result. Cluster validity selects the best result from various sets of combined knowledge. In experiments, this method was applied to nominal databases and numerical databases. The results showed that this method could produce good clustering results for both types of data. Moreover, ambiguity of a boundary of clusters is defined using roughness of the clustering result.