基于改进人工蜂群的核模糊聚类算法

针对核模糊C均值（KFCM）算法对初始聚类中心敏感、易陷入局部最优的问题，利用人工蜂群（ABC）算法的构架简单、全局收敛速度快的优势，提出了一种改进的人工蜂群算法（IABC）与KFCM迭代相结合的聚类算法。首先，以IABC求得最优解作为KFCM算法的初始聚类中心，IABC在迭代过程中将与当前维度最优解的差值的变化率作为权值，对雇佣蜂的搜索行为进行改进，平衡人工蜂群算法的全局搜索与局部开采能力；其次，以类内距离和类间距离为基础，构造出适应KFCM算法的适应度函数，利用KFCM算法优化聚类中心；最后，IABC和KFCM算法交替执行，实现最佳聚类效果。采用3组Benchmark测试函数6组UCI标准数据集进行仿真实验，实验结果表明，与基于改进人工蜂群的广义模糊聚类（IABC-KGFCM）相比，IABC-KFCM对数据集的聚类有效性指标提高1到4个百分点，具有鲁棒性强和聚类精度高的优势。

Kernel fuzzy C-means clustering based on improved artificial bee colony algorithm

Aiming at the problem that Kernel-based Fuzzy C-Means (KFCM) algorithm is sensitive to the initial clustering center and is easy to fall into the local optimum, and the fact that Artificial Bee Colony (ABC) algorithm is simple and of high global convergence speed, a new clustering algorithm based on Improved Artificial Bee Colony (IABC) algorithm and KFCM iteration was proposed. Firstly, the optimal solution was obtained by using IABC as the initial clustering center of the KFCM algorithm. IABC algorithm improved the search behavior of the employed bee with the change rate of the difference from the current dimension optimal solution in the iterative process, balancing the global search and local mining ability of the artificial bee colony algorithm. Secondly, based on within-class distance and between-class distance, the fitness function of the KFCM algorithm was constructed and the cluster center was optimized by KFCM algorithm. Finally, the IABC and KFCM algorithms were executed alternately to achieve optimal clustering. Three Benchmark test functions and six sets in UCI standard data set was used to carry out simulation experiments. The experimental results show that IABC-KFCM improves the clustering effectiveness index of data set by 1 to 4 percentage points compared to IABC-GFCM (Generalized Fuzzy Clustering algorithm based on Improved ABC), which has the advantages of strong robustness and high clustering precision.