A new fuzzy clustering algorithms based on transformed data
Hsiang-Chuan Liu, Bai-Cheng Jeng, Der-Bang Wu, Yi‐Hsiang Lo · 2009
The popular fuzzy c-means algorithm (FCM) is an objective function based clustering method. Hence, different objective function may lead to different results. The important issue is how to get a more compact and separable objective function to improve the cluster accuracy. The objective function of the well known improved algorithm, FCS, is a generalization of the FCM objective function by combining fuzzy within- and between-cluster variations. In this paper, considering a more separable data transformation, the improved new algorithm, “Fuzzy Transformed C-Mean (FTCM)”, is proposed. Three real data sets were applied to prove that the performance of the FTCM algorithm is better than the conventional FCM algorithm and the FCS algorithm.