Third IEEE International Conference on Data Mining (2003)
Nov. 19, 2003 to Nov. 22, 2003
Man Lung Yiu , University of Hong Kong, Pokfulam Road
Nikos Mamoulis , University of Hong Kong, Pokfulam Road
Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy 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 heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately.
N. Mamoulis and M. L. Yiu, "Frequent-Pattern based Iterative Projected Clustering," Third IEEE International Conference on Data Mining(ICDM), Melbourne, Florida, 2003, pp. 689.