A weighting <i>k</i>-modes algorithm for subspace clustering of categorical data

作     者：Cao, Fuyuan Liang, Jiye Li, Deyu Zhao, Xingwang 

作者机构：Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat Minist Educ Taiyuan 030006 Shanxi Peoples R China 

出 版 物：《NEUROCOMPUTING》 (神经计算)

年 卷 期：2013年第108卷

页      面：23-30页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [71031006, 70971080, 60970014] Special Prophase Project on National Key Basic Research and Development Program of China (973) [2011CB311805] Natural Science Foundation of Shanxi [2010021016-2, 2010011021-1] China Postdoctoral Science Foundation [2012M510046] 

主　　题：Subspace clustering Weight k-Modes algorithm Categorical data 

摘      要：Traditional clustering algorithms consider all of the dimensions of an input data set equally. However, in the high dimensional data, a common property is that data points are highly clustered in subspaces, which means classes of objects are categorized in subspaces rather than the entire space. Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a data set. In this paper, a weighting k-modes algorithm is presented for subspace clustering of categorical data and its corresponding time complexity is analyzed as well. In the proposed algorithm, an additional step is added to the k-modes clustering process to automatically compute the weight of all dimensions in each cluster by using complement entropy. Furthermore, the attribute weight can be used to identify the subsets of important dimensions that categorize different clusters. The effectiveness of the proposed algorithm is demonstrated with real data sets and synthetic data sets. (C) 2012 Elsevier B.V. All rights reserved.