随机学习萤火虫算法优化的模糊软子空间聚类算法

传统软子空间聚类算法在利用局部搜索策略解决等式约束的连续非线性的变量加权问题时,易陷入局部最优导致聚类效果不佳.针对该问题,该文提出了一种随机学习萤火虫算法优化的模糊软子空间聚类算法.该算法利用具有全局搜索能力的萤火虫算法对新算法的目标函数进行优化,同时,为弥补萤火虫算法易提前收敛和寻优精度较低的缺陷,对萤火虫种群进化方式和全局最优粒子的学习方式进行了改进.新算法将权值矩阵拟化成萤火虫种群,使变量加权的等式约束变为界约束,通过萤火虫位置的更新搜索最优权重并发掘子空间中隐藏的簇类.在人工数据集、UCI标准数据集和癌症基因表达数据集上的实验结果表明:该算法具有较好的聚类效果.

The Fuzzy Soft Subspace Clustering Algorithm Optimized by Random Learning Firefly Algorithm

The traditional soft subspace clustering algorithm uses local search strategy to solve the continuous nonlinear variable weighting problem with equality constraints,and is easy to fall into local optimum,resulting in poor clustering result.To solve this problem,a fuzzy soft subspace clustering algorithm optimized by random learning firefly algorithm is proposed.The firefly algorithm with global search ability is used to optimize the objective function of the new algorithm.At the same time,in order to make up for premature convergence and low precision of firefly algorithm,the evolution pattern of firefly population and the learning method of global optimal particle are improved.The new algorithm formulates the weight matrix into firefly population,and transforms equality constraints of variable weighting problem into bound constraints,updating the firefly position to search for optimal weight and to explore the hidden clusters in the subspace.The experimental results on artificial dataset,UCI standard dataset and cancer gene expression dataset show that the new algorithm has better clustering effect.