Clustering Uncertain Data Using Voronoi Diagrams and R-Tree Index

—We study the problem of clustering uncertain objects whose locations are described by probability density functions (pdf). We show that the UK-means algorithm, which generalises the k-means algorithm to handle uncertain objects, is very inefficient. The inefficiency comes from the fact that UK-means computes expected distances (ED) between objects and cluster representatives. For arbitrary pdf’s, 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 calculation. 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 organise 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 com...

• Type: Journal
• Year: 2010
• Where: TKDE
• Authors: Ben Kao, Sau Dan Lee, Foris K. F. Lee, David Wai-Lok Cheung, Wai-Shing Ho
• Keywords: Basic Bounding-box-based Technique, Probability Density Functions, TKDE 2010, Uncertain Objects