Description

Clustering approach is represented for the analysis of similarity between the information within the database of any dimension. In order to have an effective a similarity search on a high dimensional database where exists correlated data, have to improve or extend the conventional clustering methods with different approaches. There have been presented several approaches by pruning techniques, random selection, distance based clustering and so on. In this paper, we are proposing a distance based bound approach of adaptive clustering in the high dimensional database. We propose a new cluster-adaptive distance bound based on separating hyper plane boundaries of Coronoid clusters to complement our cluster based index. This bound enables efficient spatial filtering, with a relatively small preprocessing storage overhead and is applicable to Euclidean and Mahalanobis similarity measures. Thus the objective of the project is to perform clustering with exact nearest neighbor search, less number of random IOs over several recently proposed indexes, low computational cost and scales well with dimensions and size of the data set by tightening the cluster-distance bounds, possibly by optimizing the clustering algorithm so as to optimize the cluster distance bounds using optimization techniques like Pillar algorithm. Also we have evaluated the proposed technique with the existing approaches on a set of performance metrics and the experimental result shows that the proposed approach works better than the conventional methods on similarity search. Index Terms: Indexing methods, multimedia databases, clustering, similarity measures, image databases