Issue No. 09 - September (2010 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.82
Sau Dan Lee , The University of Hong Kong, Hong Kong
David Wai-lok Cheung , The University of Hong Kong, Hong Kong
Foris K.F. Lee , The University of Hong Kong, Hong Kong
Ben Kao , The University of Hong Kong, Hong Kong
Wai-Shing Ho , The University of Hong Kong, Hong Kong
We study the problem of clustering uncertain objects whose locations are described by probability density functions (pdfs). We show that the UK-means algorithm, which generalizes the k-means algorithm to handle uncertain objects, is very inefficient. The inefficiency comes from the fact that UK-means computes expected distances (EDs) between objects and cluster representatives. For arbitrary pdfs, expected distances are computed by numerical integrations, which are costly operations. We propose pruning techniques that are based on Voronoi diagrams to reduce the number of expected distance calculations. These techniques are analytically proven to be more effective than the basic bounding-box-based technique previously known in the literature. We then introduce an R-tree index to organize the uncertain objects so as to reduce pruning overheads. We conduct experiments to evaluate the effectiveness of our novel techniques. We show that our techniques are additive and, when used in combination, significantly outperform previously known methods.
Uncertainty, clustering, object hierarchies, indexing methods.
Sau Dan Lee, David Wai-lok Cheung, Foris K.F. Lee, Ben Kao, Wai-Shing Ho, "Clustering Uncertain Data Using Voronoi Diagrams and R-Tree Index", IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 1219-1233, September 2010, doi:10.1109/TKDE.2010.82