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Irrelevant attributes add noise to high-dimensional clusters and render traditional clustering techniques inappropriate. Recently, several algorithms that discover projected clusters and their associated subspaces have been proposed. In this paper, we realize the analogy between mining frequent itemsets and discovering dense projected clusters around random points. Based on this, we propose a technique that improves the efficiency of a projected clustering algorithm (DOC). Our method is an optimized adaptation of the frequent pattern tree growth method used for mining frequent itemsets. We propose several techniques that employ the branch and bound paradigm to efficiently discover the projected clusters. An experimental study with synthetic and real data demonstrates that our technique significantly improves on the accuracy and speed of previous techniques.
Database management, database applications, clustering, classification, and association rules.

N. Mamoulis and M. L. Yiu, "Iterative Projected Clustering by Subspace Mining," in IEEE Transactions on Knowledge & Data Engineering, vol. 17, no. , pp. 176-189, 2005.
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