A mathematical model of similarity and clustering

被引：0

作者：

Sun, FS ^{[1
]}

Tzeng, CH ^{[1
]}

机构：

[1] Ball State Univ, Dept Comp Sci, Muncie, IN 47306 USA

来源：

ITCC 2004: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: CODING AND COMPUTING, VOL 1, PROCEEDINGS | 2004年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces an abstract model of data similarity and clustering. A similarity on a space Q is formulated explicitly by a reflexive and symmetric binary relation, called a tolerance relation, for which we introduce three types of coverings of Q. Given a covering U, a clustering is defined to be a minimal sub-covering. To search for an optimal clustering is to minimize the number of clusters, which is intractable in general. This paper proposes a heuristic method to search for sub-optimal clusterings for a given tolerance relation.

引用

页码：460 / 464

页数：5

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