基于新适应度的粒子群算法聚类数据

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI:10.1109/DMO.2011.5976539

Ehsan Toreini, M. Mehrnejad

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引用次数: 6

摘要

数据聚类已经研究了很长时间，每天的趋势都被提出，以便在这一领域取得更好的结果。粒子群算法(PSO)在聚类中的应用是该领域的最新发展趋势之一，具有很好的改进潜力。在本文中，我们考虑了一个新的适应度函数用于我们的基于pso的聚类方法，并与之前的方法进行了比较。实验结果表明，该方法具有较好的效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Clustering data with Particle Swarm Optimization using a new fitness

Data clustering has been studied for a long time and every day trends are proposed for better outcomes in this field. One of the latest trends in this area is the application of Particle Swarm Optimization (PSO) in clustering which has good potential for improvements. In this paper, we consider a new fitness function for our PSO-based clustering method and compared it with the previous ones. Experimental results show that our method has better outcomes than the other ones.

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2011 3rd Conference on Data Mining and Optimization (DMO)

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