Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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Finding generalized projected clusters in high dimensional spaces
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Clustering by pattern similarity in large data sets
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Discovering frequent itemsets by support approximation and itemset clustering
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Mining relaxed closed subspace clusters
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Advancing data clustering via projective clustering ensembles
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Clustering very large multi-dimensional datasets with MapReduce
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An extension of the PMML standard to subspace clustering models
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A novel SVM+NDA model for classification with an application to face recognition
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Projective clustering ensembles
Data Mining and Knowledge Discovery
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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.