Title :
A comparative study on cluster validity criteria in linear fuzzy clustering and pareto optimality analysis
Author :
Honda, Katsuhiro ; Nomaguchi, Tomonari ; Notsu, Akira ; Ichihashi, Hidetomo
Author_Institution :
Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai, Japan
Abstract :
Cluster validation is an important issue in cluster analysis. In this paper, a comparative study on validity criteria is performed with linear fuzzy clustering that can be identified with a local PCA technique. Besides the standard fuzzification approach, the entropy regularization approach is responsible for fuzzification of data partition and the approach implies a close relation between FCM-type linear fuzzy clustering and probabilistic PCA models. This comparative study reveals mutual differences between two fuzzification approaches from the view point of cluster validation using several cluster validity criteria. Additional characteristics are shown in a pareto analysis, in which the effect of noise sensitivity is also discussed.
Keywords :
Pareto analysis; data analysis; fuzzy set theory; pattern clustering; principal component analysis; probability; cluster analysis; cluster validation; entropy regularization approach; linear fuzzy clustering; pareto optimality analysis; principal component analysis; probabilistic PCA model; Algorithm design and analysis; Clustering algorithms; Clustering methods; Data mining; Entropy; Fuzzy sets; Pareto analysis; Partitioning algorithms; Principal component analysis; Prototypes;
Conference_Titel :
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Conference_Location :
Jeju Island
Print_ISBN :
978-1-4244-3596-8
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2009.5277182
