改进的FCM半监督聚类算法

通过将类间分离度函数引入到模糊C-均值聚类算法中,结合半监督的思想,建立基于信息熵的半监督模糊C-均值聚类模型,并对该模型的求解过程进行推导,提出一种新的算法.为了验证算法的有效性,将该算法在UCI数据集上进行实验,实验结果表明,该算法比仅引入信息熵的模糊C-均值聚类方法聚类性能更好.

Improved Fuzzy C-Means Clustering Algorithm

A new fuzzy C-means clustering algorithm was proposed by the introduction of functions of separation betweenclusters into FCM clustering algorithm and with the nature of semi supervised learning considered. The model of semi supervised FCM clustering algorithm with the information entropy as constraints was established and the solution to the model was derived. The simulation experiments were performed on UCI data sets to verify the effectiveness of the proposed algorithm. The experimental results show that this modified algorithm gets the better validity and performance.