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

计量
- 文章访问数: 3239
- HTML全文浏览量: 72
- PDF下载量: 853
- 被引次数: 0
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.

HTML全文

参考文献(1)

[1] 蔡自兴, 徐光佑著. 人工智能及其应用. 第三版, 北京: 清华大学出版社, 2004: 10-23. [2] Cai Zi-xing and Xu Guang-you. Artifcial Intelligence: Principles and Applications Third Edition, Beijing: Tsinghua Press, 2004: 10-23. [3] Li Chao-shun, Zhou Jian-zhong, and Li Qing-qing. A fuzzy clustering algorithm based on mutative scale chaos optimization[J].Advances in Neural Networks.ISNN 2008, Berlin/Heidelberg: Springer.2008, 5264:259-267 [4] Runkler T A and Katz C. Fuzzy clustering by particle swarm optimization. Proceedings of 2006 IEEE International Conference on Fuzzy Systems. Vancouver, BC, 2006: 601-608. [5] Chuang Keh-shih, Tzeng Hong-long, and Chen Sharon. Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics. 2006, 30(1): 9-15. [6] Cai Wei-ling, Chen Song-can, and Zhang Dao-qiang. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J].Pattern Recognition.2007, 40(3):825-838 [7] Pal N R and Bezdek J C. On cluster validity for the Fuzzy c-means Model. IEEE Transactions on Fuzzy Systems. 1995, 3(3): 370-378. [8] Kamber M and Han Jia-wei. Data Mining: Concepts and Techniques. 2rd edition. Singapore: Elsevier Press. 2005: 295-300. [9] Breunig M M, Kriegel Hans-peter, and Raymond T N, et al.. LOF: Identifying density-based local outliers. Proceedings of ACM SIGMOD International Conference on Management of Data, Dallas, Texas: ACM Press. 2000, 29: 93-104. [10] Cao Hui, Si?Gang-quan,?Zhu Wen-zhi, and Zhang Yan-bin. Enhancing effectiveness of density-based outlier mining. International Symposiums on Information processing, Moscow, May 23-25, 2008. [11] Ghoting A, Parthasarathy S, and Otey M E. Fast mining of distance-based outliers in high-dimensional dataset[J].Data Mining Knowledge Discovery.2008, 16(3):349-364 [12] Weng Xiao-qing and Shen Jun-yi. Detecting outlier samples in multivariate time series dataset[J].Knowledge-Based Systems.2008, 21(8):807-812 [13] Gustafson E E and Kessel W C. Fuzzy clustering with a fuzzy covariance matrix. Proceedings of IEEE Conference on Decision Control. San Diego, Californian, Piscataway, NJ. 1979: 761-766.

施引文献

资源附件(0)

访问统计