混合自适应小生境改进粒子群优化聚类算法

Lei Jiang, L. Ding, Yunwen Lei, Ming Chen, Zhigao Zeng
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引用次数: 3

摘要

聚类是一种重要的数据分析和数据挖掘技术。粒子群聚类是一种流行的分割算法。但它经常存在过早收敛和陷入次优解的问题。本文采用自适应小生境粒子群算法改进聚类。研究了不同适应度优化函数对聚类数据的影响。结果表明,将自适应小生境与粒子群聚类技术相结合的算法更具竞争力。结果表明,本文提出的适应度优化函数在高维数据集和聚类样本数差异较大的情况下,比Merwe引入的常用函数更有前景。
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Notice of RetractionHybrid adaptive niche to improve particle swarm optimization clustering algorithm
Clustering is an important data analysis and data mining technique. PSO clustering is one of the popular partition algorithm. But it often suffers from the problem of premature convergence and traps in suboptimum solution. This paper uses adaptive niche particle swarm algorithm to improve clustering. And it is also studied the impact of different fitness optimization function to clustering data. The results show that the algorithm which hybrids adaptive niche to PSO clustering techniques has more competitive. It also shows that the new fitness optimization function we proposed is more promising in the fields of high dimension data set and large difference of the number samples in clusters than the popular function of Merwe introduced.
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