Hidetomo Ichihashi

Applicability of ICA-Based Dimension Reduction in Fuzzy c-Means-Based Classifier

Fuzzy c-Means-based Classifier (FCMC) has been proved to have high performances based on clustering concepts in conjunction with several parameter optimization methods. In general, FCMC is applied to high-dimensional data after dimension reduction by Principal Component Analysis (PCA). In this paper, the applicability of Independent Component Analysis (ICA)-based dimension reduction is investigated in the FCMC context. ICA is a computational method for separating a multivariate signal into additive subcomponents with the assumption of non-Gaussian signals. This paper compares the performance of FCMC using four data sets. Two initialization approaches of the PCA-Tree-based and k-dimensional tree (kd-Tree)-based are also compared.

Variable Selection in PCA Using Fuzzy Clustering and Its Application to Data Mining

Knowledge discovery in databases (KDD) or data mining involves fitting models to or determining patterns from high dimensional data sets, and extraction of correlation rules plays an important role. This paper proposes a new approach to knowledge discovery with linear model estimation, in which principal component analysis (PCA) is performed by selecting variables. The proposed algorithm is a hybrid of fuzzy clustering and PCA based on lower rank approximation of data matrix, in which the relative responsibilities of the variables are estimated by using possibilistic constraint for memberships. The proposed algorithm is also enhanced to a local PCA model that can be used for data mining by performing both of linear model estimation and stratified sampling.