A new clustering algorithm for categorical attributes

被引：2

作者：

Lu, SF ^{[1
]}

Lu, ZD ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan 430074, Peoples R China

来源：

JOURNAL OF UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING | 2000年 / 7卷 / 04期

关键词：

data mining; clustering; similarity;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

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 presented. Furthermore, a new clustering algorithm for categorical attributes is addressed. A single scan of the dataset yields a good clustering, and more additional passes can be used to improve the quality further.

引用

页码：318 / 322

页数：5

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