A New Clustering Algorithm for Categorical Attributes

作     者：Songfeng Lu, Zhengding Lu (College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China) 

作者机构：College of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China 

出 版 物：《International Journal of Minerals,Metallurgy and Materials》 (矿物冶金与材料学报（英文版）)

年 卷 期：2000年第14卷第4期

页      面：318-322页

核心收录：

学科分类：11[军事学] 0810[工学-信息与通信工程] 1105[军事学-军队指挥学] 08[工学] 081002[工学-信号与信息处理] 110503[军事学-军事通信学] 

主　　题：data mining clustering similarity Information 

摘      要：In traditional data clustering, similarity of a cluster of objects is measured by distance between objects. Such measures are not appropriate for categorical data. A new clustering criterion to determine the similarity between points with categorical attributes is pre- sented. Furthermore, a new clustering algorithm for categorical attributes is addressed. A single scan of the dataset yields a good clus- tering, and more additional passes can be used to improve the quality further.