Unsupervised classification of uncertain data objects in spatial databases using computational geometry and indexing techniques

Unsupervised classification called clustering is a process of organizing objects into groups whose members are similar in some way. Clustering of uncertain data objects is a challenge in spatial data bases. In this paper we use Probability Density Functions (PDF) to represent these uncertain data objects, and apply Uncertain K-Means algorithm to generate the clusters. This clustering algorithm uses the Expected Distance (ED) to compute the distance between objects and cluster representatives. To further improve the performance of UK-Means we propose a novel technique called Voronoi Diagrams from Computational Geometry to prune the number of computations of ED. This technique works efficiently but results pruning overheads. In order to reduce these in pruning overhead we introduce R-tree indexing over these uncertain data objects, so that it reduces the computational cost and pruning overheads. Our novel approach of integrating UK-Means with voronoi diagrams and R Tree applied over uncertain data objects generates imposing outcome when compared with the accessible methods.