现有聚类算法面向高维稀疏数据多未考虑类簇可重叠和离群点的存在，导致聚类效果不理想。针对此，提出一种可重叠子空间K-Means聚类算法(An Overlapping Subspace K-Means Clustering Algorithm, OS-K-Means)。给出类簇子空间计算策略，在聚类过程中动态更新每个类簇的属性子空间，并定义合理的约束函数指导聚类过程，从而实现类簇的可重叠性与寻找离群点的效果。具体地，定义合理的目标函数对传统的K-Means算法进行修正，利用熵权约束分别计算每个类簇中每个维度的权重，使用权重值来标识对不同类簇中维度的相对重要性，并加入对重叠程度和离群值数量控制的参数。
☆30Aug 29, 2019Updated 7 years ago
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