针对模糊C均值(FCM)聚类算法在数据集下聚类效果差的情况,以及基于欧氏距离的相似性度量只考虑数据点之间的局部一致性问题,提出了基于Jeffery散度相似性度量加权FCM聚类算法(JW-FCM)。引入源于Jeffery散度的相似性度量,首先,对于FCM算法进行特征加权,对数据的不同特征值赋予适当的权重,再将Jeffery散度与加权FCM算法进行结合得到JW-FCM算法。将JW-FCM算法与几种相关算法在人工数据集和UCI数据集上进行对比实验,通过实验分析与比较,证明了JW-FCM算法具有更好的收敛性、鲁棒性、准确性。实验结果表明,改进算法表现出较好的聚类效果。

In view of the poor clustering effect of the fuzzy C mean (FCM) clustering algorithm under the data set, and the similarity measure based on Euclidean distance only considers the local consistency between data points. This paper presents a weighted FCM clustering algorithm based on Jeffrey divergence similarity measure (JW-FCM), and introduces the similarity measure derived from Jeffery divergence. First, perform feature weighting on the FCM algorithm, assign appropriate weights to different feature values of the data, and then combine the Jeffery divergence with the weighted FCM algorithm to obtain the JW-FCM algorithm. The JW-FCM algorithm is compared with several related algorithms on the artificial data set and UCI data set. Through experimental analysis and comparison, it is proved that the JW-FCM algorithm has better convergence, robustness, and accuracy. The experimental results show that the improved algorithm shows better clustering effect.