Building a Decision Cluster Classification Model for High Dimensional Data by a Variable Weighting k-Means Method

Abstract

In this paper, a new classification method (ADCC) for high dimensional data is proposed. In this method, a decision cluster classification model (DCC) consists of a set of disjoint decision clusters, each labeled with a dominant class that determines the class of new objects falling in the cluster. A cluster tree is first generated from a training data set by recursively calling a variable weighting <em>k</em> -means algorithm. Then, the DCC model is selected from the tree. Anderson-Darling test is used to determine the stopping condition of the tree growing. A series of experiments on both synthetic and real data sets have shown that the new classification method (ADCC) performed better in accuracy and scalability than the existing methods of <em>k</em> -<em>NN</em> , decision tree and SVM. It is particularly suitable for large, high dimensional data with many classes.