A new fuzzy clustering algorithms based on transformed data

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...

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Bibliographic Details
Main Authors: Hsiang-Chuan Liu, Bai-Cheng Jeng, Der-Bang Wu, Yi-Hsiang Lo
Format: Conference Proceeding
Language:English
Subjects:
Asia
Bioinformatics
Clustering algorithms
Clustering methods
Cybernetics
FCM
FCS
FTCM
Fuzzy clustering algorithm
Fuzzy sets
Machine learning
Machine learning algorithms
Mathematics
Partitioning algorithms
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Summary: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.
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212627