A new fuzzy clustering algorithms based on transformed data

Author

Liu, Hsiang-chuan ; Jeng, Bai-cheng ; Wu, Der-Bang ; Lo, Yi-hsiang

Author_Institution

Dept. of Bioinf., Asia Univ., Taichung, Taiwan

Volume

5

fYear

2009

fDate

12-15 July 2009

Firstpage

3036

Lastpage

3041

Abstract

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.

Keywords

fuzzy set theory; pattern clustering; unsupervised learning; FCM; FCS; FTCM; fuzzy transformed c-means clustering algorithm; objective function; separable data transformation; unsupervised learning; Asia; Bioinformatics; Clustering algorithms; Clustering methods; Cybernetics; Fuzzy sets; Machine learning; Machine learning algorithms; Mathematics; Partitioning algorithms; FCM; FCS; FTCM; Fuzzy clustering algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2009 International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3702-3

Electronic_ISBN

978-1-4244-3703-0

Type

conf

DOI

10.1109/ICMLC.2009.5212627

Filename

5212627