改进的基于熵的中心聚类算法

Improved Entropy-based Centre Clustering Algorithm

依据基于熵的模糊聚类算法(EFC),提出一种改进的基于熵的中心聚类算法,即通过EFC算法得到差异性十分明显的原始数据集的簇心,以这些簇心为中心再次进行聚类分析,通过各点到各中心的距离将各点重新分配到以各中心所代表的集合中。改进的算法不仅可以得到具有紧凑且差异明显的聚类结果,还可以使准确率得到有效提高。实验结果表明,该改进的算法能够实现数据集的有效聚类,相比于EFC算法的聚类结果准确率更高。

This article is based on entropy-based fuzzy clustering algorithm （EFC）, proposes an improved entropy-based centre clustering algorithm, namely the very obvious differences clusters heart in the original data is obtained through EFC algorithm and these clusters heart to be heart-centered cluster is analyzed again, each point is re-assigned to the collection which the center re- presents through computing the distance between each point and center. This improved algorithm can not only get compact and significantly different clustering results, but also can effectively improve the accuracy rate. Experimental results show that the improved algorithm can realize data sets efficient clustering, with higher accuracy.