RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets

• Autorzy: Maji P. , Pal S.K.
• Języki publikacji: EN

Abstrakty

EN

A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed in this paper. It comprises a judicious integration of the principles of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of overlapping partitions. The concept of crisp lower bound and fuzzy boundary of a class, introduced in rough-fuzzy c-means, enables efficient selection of cluster prototypes. Several quantitative indices are introduced based on rough sets for evaluating the performance of the proposed c-means algorithm. The effectiveness of the algorithm, along with a comparison with other algorithms, has been demonstrated on a set of real life data sets.

Słowa kluczowe

EN

pattern recognition; data mining; clustering algorithms; fuzzy c-means; rough sets

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2007
• Tom: Vol. 80, nr 4
• Strony: 475--496
• Opis fizyczny: bibliogr. 24 poz., fot., tab., wykr.

Twórcy

autor

Maji P.

autor

Pal S.K.

• Center for Soft Computing Research. Indian Statistical Institute, 203 B.T. Road, Kolkata, West Bengal, India 700108,

Bibliografia

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