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Evaluation of Monte Carlo Subspace Clustering with OpenSubspace
David C. Hunn and Clark F. Olson In Proceedings of the 9th International Conference on Data Mining (DMIN'13), July 2013. Download (589 K) We present the results of a thorough evaluation of the subspace clustering algorithm SEPC using the OpenSubspace framework. We show that SEPC outperforms competing projected and subspace clustering algorithms on synthetic and some real world data sets. We also show that SEPC can be used to effectively discover clusters with overlapping objects (i.e., subspace clustering). |
