Automatic subspace clustering of high dimensional data for data mining applications
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SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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DUSC: Dimensionality Unbiased Subspace Clustering
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EDSC: efficient density-based subspace clustering
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Similarity Search in Arbitrary Subspaces Under Lp-Norm
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
Projective clustering ensembles
Data Mining and Knowledge Discovery
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For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering method that steers mining to few selected subspace clusters. Our novel steering technique reduces subspace processing by identifying and clustering promising subspaces and their combinations directly. Thereby, it narrows down the search space while maintaining accuracy. Thorough experiments on real and synthetic databases show that steering is efficient and scalable, with high quality results. For future work, our steering paradigm for density-based subspace clustering opens research potential for speeding up other subspace clustering approaches as well.