TITLE:
A New Integrated Fuzzifier Evaluation and Selection (NIFEs) Algorithm for Fuzzy Clustering
AUTHORS:
Chanpaul Jin Wang, Hua Fang, Sun Kim, Ann Moormann, Honggang Wang
KEYWORDS:
Fuzzifier, Fuzzy C-Means, Multiple Imputation-Based Fuzzy Clustering (MIFuzzy), Missing Data, Longitudinal Data
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.3 No.7,
June
30,
2015
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.