Achieving True Query-Cluster Distance Bound by Optimizing Clustering Algorithm
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, the authors are proposing a distance based bound approach of adaptive clustering in the high dimensional database. They propose a new cluster-adaptive distance bound based on separating hyper plane boundaries of Coronoid clusters to complement their cluster based index.