关键词: FCM聚类算法, 蝙蝠算法, 混沌映射, 样本加权, 特征加权

Abstract: 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.

Key words: FCM clustering algorithm, bat algorithm, Logistic map, sample weighted, feature weighted