Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data
2013 IEEE 13th International Conference on Data Mining (2009)
Dec. 6, 2009 to Dec. 9, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2009.10
Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters. We prove that computation of this model is NP-hard. For RESCU, we propose an approximative solution that shows high accuracy with respect to our relevance model. Thorough experiments on synthetic and real world data show that RESCU successfully reduces the result to manageable sizes. It reliably achieves top clustering quality while competing approaches show greatly varying performance.
data mining, high dimensional data, subspace clustering, redundancy removal, global optimization
Stephan Günnemann, Thomas Seidl, Ralph Krieger, Emmanuel Müller, Ira Assent, "Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 377-386, 2009, doi:10.1109/ICDM.2009.10