A Rough Set Based Clustering Method by Knowledge Combination

• OKUZAKI Tomohiro

  Graduate School of Engineering, Himeji Institute of Technology

• HIRANO Shoji

  Department of Medical Informatics, Shimane Medical University, School of Medicine

• KOBASHI Syoji

  Graduate School of Engineering, Himeji Institute of Technology

• HATA Yutaka

  Graduate School of Engineering, Himeji Institute of Technology

• TAKAHASHI Yutaka

  Graduate School of Engineering, Himeji Institute of Technology

抄録

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.

収録刊行物

• IEICE transactions on information and systems

  IEICE transactions on information and systems 85 (12), 1898-1908, 2002-12-01

  一般社団法人電子情報通信学会

キーワード

• rough sets
• clustering
• knowledge combination

詳細情報

• CRID: 1573950402231621632
• NII論文ID: 110003213607
• NII書誌ID: AA10826272
• ISSN: 09168532
• 本文言語コード: en
• データソース種別:
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