Title :
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
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;
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
DOI :
10.1109/ICMLC.2009.5212627
