期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2012
卷号：2
期号：5
页码：213-217
出版社：International Journal of Soft Computing & Engineering
摘要：Feature Selection is a preprocessing technique in supervised learning for improving predictive accuracy while reducing dimension in clustering and categorization. Multitype Feature Coselection for Clustering (MFCC) with hard k means is the algorithm which uses intermediate results in one type of feature space enhancing feature selection in other spaces, better feature set is co selected by heterogeneous features to produce better cluster in each space. Soft Clustering is an optimization technique of data analysis and pattern recognition which allocates a set of observations to cluster in a fuzzy way, constructing a membership-function matrix whose (i, j)th element represents the “the degree of belonging” of the ith observations to the jth cluster. This paper presents the empirical results of the MFCC algorithm with soft clustering and also gives the comparison results of MFCC with hard and soft k means. Fuzzy k-means clustering is proposed for getting the robustness against the outliers.
