International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 9, No. 5, 8 May 2017
Cover page and Table of Contents: PDF (size: 554KB)
Possibilistic Fuzzy Clustering for Categorical Data Arrays Based on Frequency Prototypes and Dissimilarity Measures
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Author(s)
Zhengbing Hu
1,*
Yevgeniy V. Bodyanskiy
2
Oleksii K. Tyshchenko
2
Viktoriia O. Samitova
2
Index Terms
Computational Intelligence, Machine Learning, Categorical Data, Categorical Scale, Possibilistic Fuzzy Clustering, Frequency Prototype, Dissimilarity Measure
Abstract
Fuzzy clustering procedures for categorical data are proposed in the paper. Most of well-known conventional clustering methods face certain difficulties while processing this sort of data because a notion of similarity is missing in these data. A detailed description of a possibilistic fuzzy clustering method based on frequency-based cluster prototypes and dissimilarity measures for categorical data is given.
Cite This Paper
Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Viktoriia O. Samitova,"Possibilistic Fuzzy Clustering for Categorical Data Arrays Based on Frequency Prototypes and Dissimilarity Measures", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.5, pp.55-61, 2017. DOI:10.5815/ijisa.2017.05.07
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