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 JAMP  Vol.3 No.7 , July 2015
A New Integrated Fuzzifier Evaluation and Selection (NIFEs) Algorithm for Fuzzy Clustering
Chanpaul Jin Wang1,2, Hua Fang1*, Sun Kim3, Ann Moormann4, Honggang Wang2
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Abstract: Fuzzy C-means (FCM) is simple and widely used for complex data pattern recognition and image analyses. However, selecting an appropriate fuzzifier (m) is crucial in identifying an optimal number of patterns and achieving higher clustering accuracy, which few studies have investigated. Built upon two existing methods on selecting fuzzifier, we developed an integrated fuzzifier evaluation and selection algorithm and tested it using real datasets. Our findings indicate that the consistent optimal number of clusters can be learnt from testing different fuzzifiers for each dataset and the fuzzifier with the lowest value for this consistency should be selected for clustering. Our evaluation also shows that the fuzzifier impacts the clustering accuracy. For longitudinal data with missing values, m = 2 could be an empirical rule to start fuzzy clustering, and the best clustering accuracy was achieved for tested data, especially using our multiple-imputation based fuzzy clustering.
Keywords: Fuzzifier, Fuzzy C-Means, Multiple Imputation-Based Fuzzy Clustering (MIFuzzy), Missing Data, Longitudinal Data
Cite this paper: Wang, C. , Fang, H. , Kim, S. , Moormann, A. , Wang, H. (2015) A New Integrated Fuzzifier Evaluation and Selection (NIFEs) Algorithm for Fuzzy Clustering. Journal of Applied Mathematics and Physics, 3, 802-807. doi: 10.4236/jamp.2015.37098.
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