Efficient Clustering of Uncertain Data
- 1 December 2006
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE International Conference on Data Mining (ICDM)
- No. 15504786,p. 436-445
- https://doi.org/10.1109/icdm.2006.63
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.Keywords
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