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模糊K-Harmonic Means聚类算法 被引量：6

Fuzzy K-Harmonic Means clustering algorithm
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摘要 对K-HarmonicMeans算法进行扩展,考虑到数据点对不同类的隶属关系,将模糊的概念应用到聚类中,提出了模糊K-HarmonicMeans算法,推导出聚类中心和模糊隶属度的迭代公式.在中心迭代聚类算法统一框架的基础上,推导出FKHM算法聚类中心的条件概率表达式以及在迭代过程中的数据加权函数表达式.最后,用Folkes&Mallows指标对聚类结果进行评价.实验表明,模糊K-HarmonicMeans(KHM)算法在聚类对于初值不敏感的同时提高了聚类结果的精确度,达到较好的聚类效果. Considering the fact that data belong to several clusters to some extent, we import the fuzzy membership of data to clustering analysis, and propose the fuzzy K-Harmonic Means clustering （FKHM）algorithm. The iterative expressions for cluster center and fuzzy membership are deduced respectively. We then describe a unified expression for the iteration of centers, and deduce the conditional probability expression for the centers and data weight functions for FKHM. Finally, the Folkes ＆ Mallows index is used to evaluate the clustering result. Experiment indicates that the fuzzy K-Harmonic Means algorithm can not only overcome the sensitivity to the initial centers, but also improve the quality of clustering results, compared with the K-Harmonic Means.
作者 赵恒 杨万海 张高煜
机构地区 西安电子科技大学电子工程学院
出处 《西安电子科技大学学报》 EI CAS CSCD 北大核心 2005年第4期603-606,638,共5页 Journal of Xidian University
关键词 模糊K—Harmonic Means聚类 聚类中心 条件概率 Folkes & Mallows指标 fuzzy K-Harmonic Means cluster center conditional probability Folkes ＆ Mallows index
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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参考文献8

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同被引文献28

  • 1张国英,沙芸.基于约束的粒子群聚类算法[J].计算机研究与发展,2007,44(z2):192-197. 被引量：2
  • 2刘靖明,韩丽川,侯立文.基于粒子群的K均值聚类算法[J].系统工程理论与实践,2005,25(6):54-58. 被引量：122
  • 3张向荣.基于选择性特征融合与集成学习的SAR图像分类与分割[D].西安:西安电子科技大学,2006.
  • 4Xie X L, Beni G. A Validity Measure for Fuzzy Clustering[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1991(13) : 841-847.
  • 5Dy J G, Brodley C E, Kak A, et al. Unsupervised Feature Selection Applied to Content-based Retrieval of Lung Images [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003(25): 373-378.
  • 6Runarsson T P, Yao X. Stochastic Ranking for Constrained Evolutionary Optimization[J]. IEEE Trans on Evolutionary Computation, 2000, 4(3): 284-294.
  • 7尚荣华,马文萍,焦李成,张伟.用于求解多目标优化问题的克隆选择算法[J].西安电子科技大学学报,2007,34(5):716-721. 被引量：8
  • 8Han J W, Kamber M. Data Mining: Concepts and Techniques [M]. 2nd Ed. San Francisco: Morgan Kaufmann Publishers, 2001: 398-440.
  • 9Pena J M, Lozano J A, Larranaga P. An Empirical Comparison of Four Initialization Methods for the K-Means Algorithm[J]. Pattern Recognition Letters, 1999, 20(10): 1027-1040.
  • 10Frey B J, Dueck D. Clustering by Passing Messages between Data Points [J]. Science 2007, 315(5814) : 972-976.

引证文献6

二级引证文献35

西安电子科技大学学报
2005年 第4期

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