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K平面聚类算法的模糊改进及其鲁棒性研究 被引量：2

Improved Fuzzy Partitions for K-Plane Clustering Algorithm and Its Robustness Research
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摘要 该文针对K平面聚类算法KPC(K-Plane Clustering)对噪声点敏感的缺陷,通过引入隶属度约束函数,推导出鲁棒的改进分割K平面聚类算法IFP-KPC(Improved Fuzzy Partitions for K-Plane Clustering),并利用Voronoi距离对IFP-KPC算法的鲁棒性进行了合理解释。实验结果表明IFP-KPC算法较之于KPC算法具有更好的聚类效果。 A new robust Improved Fuzzy Partitions for K-Plane Clustering （IFP-KPC） algorithm is proposed. The proposed algorithm can reduce the sensitivity of the k-plane clustering algorithm to noises in real datasets. Also the distances to the Voronoi cell are used to give a reasonable explanation for the robustness of IFP-KPC. Experimental results demonstrate the effectiveness of IFP-KPC.
作者 朱林 王士同 潘永惠 韩斌
机构地区 江南大学信息工程学院
出处 《电子与信息学报》 EI CSCD 北大核心 2008年第8期1923-1927,共5页 Journal of Electronics & Information Technology
基金 国家863项目(2006AA10Z313) 国家自然科学基金(60225015) 国防应用基础研究基金项目(A1420461266) 2005年教育部科学研究重点基金项目(105087)资助课题
关键词 K平面聚类 改进模糊分割 Voronoi距离 鲁棒性 K-plane clustering Improved fuzzy partitions Voronoi distance Robustness
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献13

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二级参考文献6

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共引文献2

同被引文献25

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引证文献2

二级引证文献6

电子与信息学报
2008年 第8期

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