基于数据加权策略的模糊聚类改进算法

唐成龙; 王石刚; 徐威

doi:10.3724/SP.J.1146.2009.00857

基于数据加权策略的模糊聚类改进算法

doi: 10.3724/SP.J.1146.2009.00857

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出版历程
- 收稿日期: 2009-06-05
- 修回日期: 2010-01-07
- 刊出日期: 2010-06-19

Improved Fuzzy Clustering Algorithm Based on Data Weighted Approach

摘要

摘要: 该文提出了一种数据指数加权的模糊均值聚类策略，引入了指数权因子和影响指数，使得可以在聚类过程中差异化处理各个数据。新策略和现有的Gustafson-Kessel(G-K)算法相结合，提出了一种新的模糊聚类算法DWG-K用于提高聚类质量和挖掘离群点。数据试验表明DWG-K在提高聚类质量方面优于现有的G-K；在离群点挖掘方面，DWG-K对离群点的判定是全局的，离群点的物理意义清楚，且计算效率明显高于当前广泛采用的基于密度的离群点挖掘算法。
- 模糊聚类; 数据加权策略; 数据加权G-K; 离群点挖掘
Abstract: A new data exponent weighted fuzzy clustering approach is proposed by introducing a set of exponent weighting factors and influence exponent, the new approach makes it possible to treat the data points discriminatively. The new approach is combined with the existing Gustafson-Kessel (G-K) algorithm and a new algorithm, DWG-K is presented. Numerical experiments show that the DWG-K is better than G-K in improving the quality of clustering, and in the outliers mining, DWG-K detects the outliers with the global view and the physical meaning of outliers is clearer, and moreover, the computational efficiency is significantly higher than the current widely used density-based method.

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