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.
In REF ] various pruning methods to avoid the expensive expected distance calculation are introduced.
Efficient Clustering of Uncertain Data
Sixth International Conference on Data Mining (ICDM'06)
Sixth International Conference on Data Mining (ICDM'06)
Computer Science