| Document Date: 2015-02-08 05:37:02
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City Springer / London / / Company DBSCAN / / Country United States / Singapore / / / Facility Data Exploration Group RWTH Aachen University / / IndustryTerm subspace/projected clustering algorithms / data mining applications / depth-first processing / jump processing / subspace clustering exploration tools / exponential search space / breadth-first processing / projected clustering algorithms / depth-first processing methods / greedy processing showing / data mining / approximative solutions / basic solution / recent applications / approximative efficient algorithms / algorithmic solutions / projected clustering algorithm / subspace mining / subspace clustering algorithms / monte carlo algorithm / / Organization Seidl Data Management and Data Exploration Group RWTH Aachen University / German Research Foundation / / Person Thomas Seidl / Morgan Kaufmann / Emmanuel Muller / / Position author / General research questions / researcher / judge / / Product OpenSubspace / OpenSubspace tool / / ProvinceOrState New York / / Technology projected clustering algorithm / subspace clustering algorithms / subspace/projected clustering algorithms / density-based algorithm / monte carlo algorithm / data mining / Machine Learning / projected clustering algorithms / / URL http /
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Mining Subspace Clusters: Enhanced Models, Efficient Algorithms and an Objective Evaluation Study Emmanuel Muller ¨ supervised by Prof. Thomas Seidl Data Management and Data Exploration Group