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Hong Kong
Dec. 18, 2006 to Dec. 22, 2006
ISBN: 0-7695-2701-7
pp: 436-445
Wang Kay Ngai , The University of Hong Kong, Hong Kong
Ben Kao , The University of Hong Kong, Hong Kong
Chun Kit Chui , The University of Hong Kong, Hong Kong
Reynold Cheng , Hong Kong Polytechnic University, Hong Kong
Michael Chau , The University of Hong Kong, Hong Kong
Kevin Y. Yip , Yale University, USA
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
Wang Kay Ngai, Ben Kao, Chun Kit Chui, Reynold Cheng, Michael Chau, Kevin Y. Yip, "Efficient Clustering of Uncertain Data", ICDM, 2006, Sixth International Conference on Data Mining (ICDM'06), Sixth International Conference on Data Mining (ICDM'06) 2006, pp. 436-445, doi:10.1109/ICDM.2006.63
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