Fuzzy clustering algorithms for mixed feature variables |
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| Institution: | 1. Department of Neurosurgery, The First People''s Hospital of Fuyang District of Hangzhou City, 429 Beihuan Road, Fuyang District, Hangzhou 311400, China;2. Department of Neurology, The First People''s Hospital of Fuyang District of Hangzhou City, 429 Beihuan Road, Fuyang District, Hangzhou 311400, China;3. Department of Emergency Medicine, The First People''s Hospital of Fuyang District of Hangzhou City, 429 Beihuan Road, Fuyang District, Hangzhou 311400, China |
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| Abstract: | This paper presents fuzzy clustering algorithms for mixed features of symbolic and fuzzy data. El-Sonbaty and Ismail proposed fuzzy c-means (FCM) clustering for symbolic data and Hathaway et al. proposed FCM for fuzzy data. In this paper we give a modified dissimilarity measure for symbolic and fuzzy data and then give FCM clustering algorithms for these mixed data types. Numerical examples and comparisons are also given. Numerical examples illustrate that the modified dissimilarity gives better results. Finally, the proposed clustering algorithm is applied to real data with mixed feature variables of symbolic and fuzzy data. |
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