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2013 IEEE 13th International Conference on Data Mining (2006)
Hong Kong
Dec. 18, 2006 to Dec. 22, 2006
ISSN: 1550-4786
ISBN: 0-7695-2701-9
pp: 436-445
Chun Kit Chui , The University of Hong Kong, Hong Kong
Wang Kay Ngai , The University of Hong Kong, Hong Kong
Kevin Y. Yip , Yale University, USA
Michael Chau , The University of Hong Kong, Hong Kong
Reynold Cheng , Hong Kong Polytechnic University, Hong Kong
Ben Kao , The University of Hong Kong, Hong Kong
ABSTRACT
We study the problem of clustering data objects whose locations are uncertain. A data object is represented by an uncertainty region over which a probability density function (pdf) is defined. One method to cluster uncertain objects of this sort is to apply the UK-means algorithm, which is based on the traditional K-means algorithm. In UK-means, an object is assigned to the cluster whose representative has the smallest expected distance to the object. For arbitrary pdf, calculating the expected distance between an object and a cluster representative requires expensive integration computation. We study various pruning methods to avoid such expensive expected distance calculation.
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CITATION
Chun Kit Chui, Wang Kay Ngai, Kevin Y. Yip, Michael Chau, Reynold Cheng, Ben Kao, "Efficient Clustering of Uncertain Data", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 436-445, 2006, doi:10.1109/ICDM.2006.63
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