NEW APPROACHES TO CLUSTERING DATA Using the Particle Swarm Optimization Algorithm

被引：0

作者：

Abdalla Esmin, Ahmed Ali ^{[1
]}

Pereira, Dilson Lucas ^{[1
]}

机构：

[1] Fed Univ Lavras UFLA, Dept Comp Sci DCC, Lavras, MG, Brazil

来源：

ICEIS 2008: PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL AIDSS: ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS | 2008年

关键词：

PSO; Data clustering;

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 presents a new proposal for data clustering based on the Particle Swarm Optimization Algorithm (PSO). In the PSO algorithm, each individual in the population searches for a solution taking into account the best individual in a certain neighbourhood and its own past best solution as well. In the present work, the PSO algorithm was adapted by using different finenesses functions and considered the situation where the data is uniformly distributed. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The proposed method was applied in an unsupervised fashion to a number of benchmark classification problems and in order to evaluate its performance.

引用

页码：593 / 597

页数：5

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