基于密度函数加权的模糊C均值聚类算法研究

Research on Fuzzy C-Means clustering algorithm based on density function weighted

模糊聚类算法具有较强的实用性,但传统模糊C均值算法(FCM)具有对样本集进行等划分趋势的缺陷,没有考虑不同样本的实际分布对聚类效果的影响,当数据集中各样本密集程度相差较大时,聚类结果不是很理想。因此,提出一种基于密度函数加权的模糊C均值聚类算法(DFCM算法),该算法利用数据对象的密度函数作为每个数据点权值。实验结果表明,与传统的模糊C均值算法相比,DFCM算法具有较好的聚类效果。

Fuzzy clustering algorithm has a strong practicality, but the traditional Fuzzy C-Means （FCM） algorithm has limitation of equal partition trend for data sets, without considering the effect of clustering produced by actual distribution of the different samples. When all kinds of samples of data set have difference intensity, the clustering result is not very satisfactory. Therefore, this paper presents Fuzzy C-Means algorithm based on a Density function weighted （DFCM algorithm）. The algorithm uses the data object density function as a weight for each data point. Experimental results show that, compared with the traditional Fuzzy C-Means algorithm, DFCM algorithm has bet- ter clustering results.