A new heuristic possibilistic clustering algorithm for feature selection

• Autorzy: Kacprzyk J. , Owsinski J. W. , Viattchenin D. A.

Identyfikatory

DOI

10.14313/JAMRIS_2-2014/18

• Języki publikacji: EN

Abstrakty

EN

The paper deals with the problem of selection of the most informative features. A new effective and efficient heuristic possibilistic clustering algorithm for feature selection is proposed. First, a brief description of basic concepts of the heuristic approach to possibilistic clustering is provided. A technique of initial data preprocessing is described and a fuzzy correlation measure is considered. The new algorithm is described and then illustrated on the well-known Iris data set benchmark and the results obtained are compared with those by using the conventional, well-known and widely employed method of principal component analysis (PCA). Conclusions and suggestions for future research are given.

Słowa kluczowe

EN

feature selection; fuzzy correlation measure; possibilistic clustering; heuristic possibilistic clustering; fuzzy cluster

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2014
• Tom: Vol. 8, No. 2
• Strony: 40--46
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Kacprzyk J.

• Systems Research Institute, Polish Academy of Sciences 6 Newelska St., 01-447 Warsaw, Poland

autor

Owsinski J. W.

• Systems Research Institute, Polish Academy of Sciences 6 Newelska St., 01-447 Warsaw, Poland

autor

Viattchenin D. A.

• United Institute of Informatics Problems, National Academy of Sciences of Belarus 6 Surganov St., 220012 Minsk, Belarus

Bibliografia

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