ACODF: a novel data clustering approach for data mining in large databases

被引：57

作者：

Tsai, CF ^{[1
]}

Tsai, CW ^{[1
]}

Wu, HC ^{[1
]}

Yang, T ^{[1
]}

机构：

[1] Natl Pingtung Univ Sci & Technol, Dept Management Informat Syst, Pingtung 91201, Taiwan

来源：

JOURNAL OF SYSTEMS AND SOFTWARE | 2004年 / 73卷 / 01期

关键词：

clustering; data mining; SOM; K-means; ant system;

D O I：

10.1016/S0164-1212(03)00216-4

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

In this paper, we present an efficient clustering approach for large databases. Our simulation results indicate that the proposed novel clustering method (called ant colony optimization with different favor algorithm) performs better than the fast self-organizing map (SOM) combines K-means approach (FSOM+K-means) and genetic K-means algorithm (GKA). In addition, in all the cases we studied, our method produces much smaller errors than both the FSOM+K-means approach and GKA. (C) 2003 Elsevier Inc. All rights reserved.

引用

页码：133 / 145

页数：13

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