基于免疫粒子群优化的模糊C均值聚类算法

把免疫系统的免疫信息处理机制引入到粒子群优化（PSO）算法中，并与模糊C均值（FCM）算法相结合提出一种新的模糊聚类算法．新算法用免疫粒子群优化算法代替FCM算法的基于梯度下降的迭代过程，使算法具有较强的全局搜索能力，很大程度上避免了FCM算法易陷入局部极小的缺陷，同时也降低了FCM算法对初始值的敏感度．采用对当基思想初始化种群，获得更优的初始候选解，提高算法聚类过程中的收敛速度．以UCI机器学习数据库中的两组数据集为研究对象，实验结果表明，该算法优于基于PSO的模糊C均值聚类算法和FCM算法．

Fuzzy C-means clustering algorithm based on immune particle swarm optimization

By combining the properties of both Particle Swarm Optimization(PSO) algorithm in which the immune information processing mechanism of immune system is involved and Fuzzy C-Means(FCM) method,a novel fuzzy clustering algorithm is proposed.The iteration process is replaced by the PSO algorithm with immunity based on the gradient descent of FCM,which makes the algorithm have a strong global searching capacity and avoids the local minimum problems of FCM.At the same time,FCM is no longer a large degree dependent on the initialization values.Moreover,it employs opposition-based learning for population initialization to obtain fitter starting candidate solutions and improve the convergence speed.A real application in classifying two data sets in UCI machine learning database is provided.Numerical experiments show that the proposed algorithm is better than fuzzy c-means clustering based on PSO and FCM.