期刊
NEUROCOMPUTING
卷 158, 期 -, 页码 81-89出版社
ELSEVIER
DOI: 10.1016/j.neucom.2015.02.002
关键词
Clustering; Uncertain data; Probability density function; Large margin; Histogram intersection kernel
资金
- Research Grants Council of the Hong Kong Special Administrative Region, China [PolyU 5182/08E, PolyU 5191/09E]
- National Natural Science Foundation of China [61105054, 61222210, 61308027, 71101096]
In this paper, the problem of clustering uncertain objects whose locations are uncertain and described by probability density functions (pdf) is studied. Though some existing methods (i.e. K-means, DBSCAN) have been extended to handle uncertain object clustering, there are still some limitations to be solved. K-means assumes that the objects are described by reasonably separated spherical balls. Thus, UK-means based on K-means is limited in handling objects which are in non-spherical shape. On the other hand, the probability density function is an important characteristic of uncertain data, but few existing clustering methods consider the difference between objects relying on probability density functions. Therefore, in this article, a clustering algorithm based on probability distribution similarity is proposed. Our method aims at finding the largest margin between clusters to overcome the limitation of UK-means. Extensively experimental results verify the performance of our method by effectiveness, efficiency and scalability on both synthetic and real data sets. (C) 2015 Elsevier B.V. All rights reserved.
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