K平面聚类算法的模糊改进及其鲁棒性研究
doi: 10.3724/SP.J.1146.2006.02065
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。Abstract: 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.
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