Title :
Cluster validation in linear fuzzy clustering of relational data from multi-cluster principal coordinate analysis view point
Author :
Haga, Naoki ; Honda, Katsuhiro ; Notsu, Akira ; Ichihashi, Hidetomo
Author_Institution :
Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai, Japan
Abstract :
This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluster MDS model. In the new cluster validation approach, the quality of fuzzy partitions is measured from the multi-cluster principal coordinate analysis view point, in which the reconstructed low dimensional substructure in each cluster is compared with the result of principal coordinate analysis considering fuzzy membership degrees to the cluster.
Keywords :
fuzzy set theory; pattern clustering; principal component analysis; cluster validation approach; fuzzy membership degree; linear fuzzy clustering; local PCA; multicluster MDS model; multicluster principal coordinate analysis view point; multidimensional scaling; relational data clustering model; Clustering algorithms; Clustering methods; Coordinate measuring machines; Covariance matrix; Linearity; Matrix decomposition; 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.2009.5277418
