Frequent-Pattern based Iterative Projected Clustering

Speaker: Ken YIU

Abstract

Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Recently, several algorithms that discover projected clusters and their associated subspaces have been proposed. In this paper, we realize that there are some analogues between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present two heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; as opposed to previous methods, they are scalable and discover projected clusters accurately.

Referred Papers