Rough set based incremental clustering of interval data

被引：41

作者：

Asharaf, S ^{[1
]}

Murty, MN ^{[1
]}

Shevade, SK ^{[1
]}

机构：

[1] Indian Inst Sci, Bangalore 560012, Karnataka, India

来源：

PATTERN RECOGNITION LETTERS | 2006年 / 27卷 / 06期

关键词：

clustering; leader; interval data; rough set;

D O I：

10.1016/j.patrec.2005.09.018

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 a novel incremental approach to clustering interval data. The method employs rough set theory to capture the inherent uncertainty involved in cluster analysis. Our experimental results show that it produces meaningful cluster abstractions for interval data at a minimal computational expense. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：515 / 519

页数：5

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