模糊C均值算法的聚类有效性评价

摘要

The clustering quality of fuzzy C-means ( FCM) clustering algorithm is affected by several factors, such as initial setting of cluster centroid, the number of clusters and fuzzy index. In this paper, a comparative study on recently published five clustering validity measurement in different application fields is presented, e. g. , different dimension of data, different cluster number and different fuzzy index. The experimental results show that the validity index based on ratio of within-class compactness and between-class separation is robust to data dimension and noise, and the validity index based on degree of membership can be applied to dataset with low dimension. The research results provide researchers with an option of selecting a suitable fuzzy clustering validity index for different application environments.%模糊C均值( FCM)聚类算法最终形成的聚类质量会受到初始值的设定、簇的个数选定及参数选择等多方面因素的影响。文中对最近发表的5种代表性聚类有效性指数在不同的数据维数、聚类个数和参数等条件下对FCM的聚类有效性评价结果进行对比分析。实验结果表明基于类内紧致度和类间离散度比值的聚类有效性指数对数据维度及噪声较为鲁棒，基于隶属度的聚类有效性指数不适于高维数据等，上述结果可帮助研究人员在不同的应用环境下选择合适的模糊聚类有效性函数。