基于改进蝙蝠算法优化的FCM聚类算法

针对传统模糊C-均值（Fuzzy C-Means, FCM）聚类算法隐含假设各个样本和各维属性对聚类结果作用相同，导致算法聚类性能降低，以及对初始中心点敏感且易陷入局部最优的问题，提出一种基于改进蝙蝠算法优化的FCM聚类算法。该算法首先采用混沌映射和速度权重来改进蝙蝠算法，然后利用改进蝙蝠算法确定FCM算法的初始聚类中心，最后根据各个样本和各维属性对聚类结果作用不同，采用样本和属性加权法对FCM算法的目标函数重新设计。实验结果表明，改进算法表现出较好的聚类效果。

An Optimal FCM Clustering Algorithm Based on Improved Bat Algorithm

Aiming at the traditional fuzzy C-means (FCM) clustering algorithm implicitly assumes that each sample and each dimension attribute have the same effect on the clustering results, which leads to the degradation of the clustering performance, and is sensitive to the initial center point and easy to fall into a local optimization, an optimal FCM clustering algorithm based on improved bat algorithm is proposed. Firstly, this algorithm improves the bat algorithm by using Logistic map and velocity weight. Secondly, the improved bat algorithm is used to determine the initial clustering center of FCM algorithm. Finally, according to the different effects of each sample and each dimension attribute on the clustering results, the objective function of FCM algorithm is redesigned by using the sample and attribute weighted method. Contrast experimental results show that the improved algorithm has better clustering effect.