作　　者： ; ; ;

机构地区： 中山大学信息科学与技术学院

出　　处： 《计算机学报》 2012年第10期2116-2128,共13页

摘　　要： 软子空间聚类算法的性能主要取决于其目标函数和搜索策略.文中提出了一种基于差分演化算法的软子空间聚类算法DESC.首先,设计了一个结合模糊加权类内相似性和界约束权值矩阵的新目标函数.然后,提出了新的隶属度计算方法.最后,引入了一种有效的全局搜索算法——复合差分演化算法,并运用该算法优化新目标函数和搜索子空间中的聚类.实验表明,新目标函数和复合差分演化算法的引入有效地提高了软子空间聚类算法的性能,新算法较已有软子空间聚类算法有明显优势. The performance of soft subspace clustering largely depends on the objective function and the search strategy. This paper presents a differential evolution （DE） based algorithm for subspace clustering. In the proposed algorithm, a novel objective function is firstly designed hy considering the fuzzy weighting within-cluster compactness and loosening the constraints o[ di- mension weight matrix. Then, a novel membership between a data point and a cluster is pro- posed. At last, an efficient global search strategy, composite DE, is introduced to optimize the proposed objective function to search subspace clusters. The simulation results show that both the proposed objective function and the introduced DE search strategy contribute to the performance enhancement of soft subspace clustering, and thus the proposed algorithm is significantly better than existing algorithrns.

关 键 词： 高维数据 子空间聚类 差分演化 模糊聚类 文本分类

领　　域： [自动化与计算机技术] [自动化与计算机技术]