A Rough Set Based Clustering Method by Knowledge Combination(Regular Section)
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概要
- 論文の詳細を見る
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.
- 社団法人電子情報通信学会の論文
- 2002-12-01
著者
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Takahashi Yutaka
Graduate School of Informatics,Kyoto University
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Hata Yutaka
Graduate School Of Engineering Himeji Institute Of Technology
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Kobashi Syoji
Graduate School Of Engineering Himeji Institute Of Technology
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Hirano Shoji
Department Of Medical Informatics Shimane Medical University School Of Medicine
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OKUZAKI Tomohiro
Graduate School of Engineering, Himeji Institute of Technology
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Okuzaki Tomohiro
Graduate School Of Engineering Himeji Institute Of Technology
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Takahashi Y
Kagoshima Univ. Kagoshima‐shi Jpn
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Takahashi Yutaka
Graduate School Of Informatics Kyoto University
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