A new ant colony clustering algorithm based on DBSCAN

被引：0

作者：

Liu, S ^{[1
]}

Dou, ZT ^{[1
]}

Li, F ^{[1
]}

Huang, YL ^{[1
]}

机构：

[1] Nankai Univ, Coll Informat Sci & Technol, Lab Intelligent Informat Proc, Tianjin 300071, Peoples R China

来源：

PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年

关键词：

AntClass; DBSCAN; ant colony clustering algorithm; cluster analysis;

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：

The AntClass algorithm is a new algorithm applying ant colony clustering algorithm, to cluster analysis, and the result is sati*ing. To attack the slow speed of the AntClass algorithm, a new algorithm named DBAntCluster is proposed. First the high density clusters are got in the dataset by using DBSCAN algorithm, and then these high density clusters are scattered in the grid board as a special kind of data object with other single data objectsIn the dataset. in DBAntCluster algorithm, the ants can avoid many unnecessary movements by using the data attribute. of density and distribution well, and the speed is greatly accelerated. This improvement is validated in our experiments.,

引用

页码：1491 / 1496

页数：6

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