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
Clustering techniques for large data sets—from the past to the future
KDD '99 Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
Analyzing Quantitative Databases: Image is Everything
Proceedings of the 27th International Conference on Very Large Data Bases
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We are proposing a novel method that makes it possibleto analyze high dimensional data with arbitrary shapedprojected clusters and high noise levels. At the core of ourmethod lies the idea of subspace validity. We map the datain a way that allows us to test the quality of subspaces usingstatistical tests. Experimental results, both on synthetic andreal data sets, demonstrate the potential of our method.