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Clustering high-dimensional data
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#	Item
111	ASCLU: Alternative Subspace Clustering Stephan Günnemann Ines Färber Emmanuel Müller Add to Reading List Source URL: eecs.oregonstate.edu Language: English - Date: 2010-06-04 02:03:54 Correlation clustering K-means clustering Computer cluster Segmentation Consensus clustering SUBCLU Statistics Cluster analysis Clustering high-dimensional data
112	Less is More: Non-Redundant Subspace Clustering Ira Assent ◦ ◦ Emmanuel Müller • Add to Reading List Source URL: eecs.oregonstate.edu Language: English - Date: 2010-06-04 02:07:16 Cluster analysis Data mining Geostatistics Projection Dimension reduction Clustering high-dimensional data SUBCLU Statistics Machine learning Multivariate statistics
113	Less is More: Non-Redundant Subspace Clustering Ira Assent ◦ Add to Reading List Source URL: eecs.oregonstate.edu Language: English - Date: 2010-09-10 15:10:23 Cluster analysis Data mining Projection Computer cluster Clustering high-dimensional data Statistics Computing Data analysis
114	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 Add to Reading List Source URL: dme.rwth-aachen.de Language: English - Date: 2015-02-08 05:37:02 Multivariate statistics Machine learning Clustering high-dimensional data SUBCLU DBSCAN Correlation clustering Data mining Principal component analysis Segmentation Statistics Cluster analysis Data analysis
115	Density-based Projected Clustering over High Dimensional Data Streams Add to Reading List Source URL: siam.omnibooksonline.com Language: English - Date: 2012-03-30 09:05:46 Multivariate statistics Data mining Geostatistics BIRCH DBSCAN K-means clustering Correlation clustering Clustering high-dimensional data Consensus clustering Statistics Cluster analysis Data analysis
116	When Pattern Met Subspace Cluster a Relationship Story Jilles Vreeken1 and Arthur Zimek2 1 Advanced Database Research and Modeling, Universiteit Antwerpen, Belgium Add to Reading List Source URL: ceur-ws.org Language: English - Date: 2011-08-29 12:12:40 Data mining Geostatistics SUBCLU Biclustering Clustering high-dimensional data Association rule learning DBSCAN Computer cluster Consensus clustering Statistics Cluster analysis Data analysis
117	Submitted to the Annals of Statistics SPECTRAL CLUSTERING AND THE HIGH-DIMENSIONAL STOCHASTIC BLOCKMODEL By Karl Rohe, Sourav Chatterjee and Bin Yu University of California Berkeley Add to Reading List Source URL: statistics.berkeley.edu Language: English - Date: 2014-03-15 03:15:37 Networks Data analysis Multivariate statistics Singular value decomposition Network analysis Spectral clustering Girvan–Newman algorithm Cluster analysis Modularity Statistics Mathematics Algebra
118	doi:[removed]j.ijar[removed] Add to Reading List Source URL: www.inf.uni-konstanz.de Language: English - Date: 2010-11-08 10:11:58 Fuzzy clustering K-means clustering DBSCAN K-medoids Multiverse Consensus clustering Clustering high-dimensional data Statistics Cluster analysis Multivariate statistics
119	Data Min Knowl Disc[removed]:130–152 DOI[removed]s10618[removed]Learning in parallel universes Bernd Wiswedel · Frank Höppner · Michael R. Berthold Add to Reading List Source URL: www.inf.uni-konstanz.de Language: English - Date: 2013-11-18 10:10:58 Data mining Geostatistics Fuzzy clustering Clustering high-dimensional data Correlation clustering K-means clustering Multiverse Categorization Parallel universe Statistics Cluster analysis Machine learning
120	Fast Approximate k-Means via Cluster Closures∗ Jing Wang† † Peking University Jingdong Wang‡ Qifa Ke§ Gang Zeng† Shipeng Li‡ Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2012-03-30 05:59:51 Cluster analysis Data mining Machine learning Geostatistics K-means clustering Nearest neighbor search Clustering high-dimensional data Consensus clustering Statistics Computational statistics Information science

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