Release Time:2019-03-12  Hits:

Indexed by: Conference Paper

Date of Publication: 2014-07-27

Included Journals: Scopus、EI

Volume: 3

Page Number: 2191-2197

Abstract: 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.