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.
Ngai et al. REF proposed the UK-means method that enhances the K-means algorithm to handle data uncertainty.
Efficient Clustering of Uncertain Data
Sixth International Conference on Data Mining (ICDM'06)
Sixth International Conference on Data Mining (ICDM'06)
Computer Science