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一种基于PIM与核方法的模糊聚类新算法

A New Fuzzy Clustering Algorithm Based on PIM and the Kernel Method
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摘要 本文针对模糊C均值聚类在大数据量时收敛较慢以及不能对多种数据结构有效聚类的缺点,结合PIM算法与核方法提出了一种新的高效聚类算法———KPIM算法,并从理论上证明了该算法的收敛性。最后利用标准实验数据IRIS数据集测试,结果表明KPIM算法在保证收敛速度的同时,聚类效果更有效。 The traditional ＂fuzzy＂ clustering （FCM） converges slowly when confronted with a large number of data points, meanwhile it can＇t deal with non - hyper spherical data structure, which compel us to present a new fuzzy clustering algorithm - the KPIM algorithm based on partition index maximization （PIM） algorithm and the kernel method. As well the paper proves convergence theorem of the new algorithm. The results of experiments on the real data show that the KPIM algorithm can effectively cluster on data with diversiform structures while guaranteeing the computation time in contrast to other previous algorithms.
作者 关忠诚 熊慧琴
机构地区 中国科学院科技政策与管理科学研究所
出处 《中国管理科学》 CSSCI 2006年第3期76-79,共4页 Chinese Journal of Management Science
关键词 模糊 聚类 PIM算法 核方法 KPIM算法 fuzzy clustering PIM kernel method KPIM algorithm
分类号 C931 [经济管理—管理学]
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参考文献16

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共引文献170

中国管理科学
2006年 第3期

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