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
收藏
页码：318 / 322
页数：5
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