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发布时间：2019-03-12

论文类型：会议论文

发表时间：2014-07-27

收录刊物：Scopus、EI

卷号：3

页面范围：2191-2197

摘要：Density-based techniques seem promising for handling data uncertainty in uncertain data clustering. Nevertheless, some issues have not been addressed well in existing algorithms. In this paper, we firstly propose a novel density-based uncertain data clustering algorithm, which improves upon existing algorithms from the following two aspects: (1) it employs an exact method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in previous work; (2) it introduces new definitions of core object probability and direct reachability probability, thus reducing the complexity and avoiding sampling. We then further improve the algorithm by using a novel assignment strategy to ensure that every object will be assigned to the most appropriate cluster. Experimental results show the superiority of our proposed algorithms over existing ones. Copyright ? 2014, Association for the Advancement of Artificial Intelligence.