【摘要】 聚类集成旨在通过融合多个不同的基聚类结果得到一个统一的类簇划分.针对现实环境中的模糊和不确定性数据,本文提出了一种基于阴影集的多粒度三支聚类集成算法.算法首先使用FCM聚类产生一组有差异性的基聚类成员,并通过阴影集构造三支聚类.然后引入多粒度粗糙集构建了四个近似集合,将每一个类簇划分为一个核心域和三个边界域.最后对边界域中的数据依次划分到核心域中,无法划分的对象则留在边界域,最终得到了三支聚类集成的结果.实验结果表明,本算法在准确率、调整兰德系数和归一化互信息方面,与多种现有的聚类集成算法相比得到了更好的聚类集成结果.更多还原

【Abstract】 The purpose of clustering ensemble is to find a unified partition of objects by fusing a set of clustering results. This paper proposes a multi-granulation three-way clustering ensemble algorithm based on shadowed sets to deal with the fuzzy and uncertainty data in the actual world. First, the algorithm generates a set of clustering members using the fuzzy c-means algorithm, and then the membership degree is mapped into three regions to construct three-way clustering. Second,the multi-granulation rough sets are used to construct four different approximate regions. Each cluster contains a core region and three boundary regions. Finally, the shadowed set is used to classify objects in boundary regions sequentially. Objects that cannot be divided are left in the boundary region. The experimental results show the algorithm obtains better clustering ensemble results in accuracy, adjust rand index, and normalized mutual information compared to multiple existing clustering ensemble algorithms.更多还原