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SUBCLU
Results: 18

#	Item
11	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
12	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
13	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
14	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
15	An Efficient Algorithm for Mining Coherent Patterns from Heterogeneous Microarrays Add to Reading List Source URL: www.cs.ucla.edu Language: English - Date: 2007-11-08 12:00:53 Microarrays Bioinformatics Cluster analysis Gene expression Computational biology Biclustering Gene expression profiling DNA microarray SUBCLU Statistics Biology Science
16	8 Cluster Analysis: Basic Concepts and Algorithms Cluster analysis divides data into groups (clusters) that are meaningful, useful, or both. If meaningful groups are the goal, then the clusters should capture the Add to Reading List Source URL: www-users.cs.umn.edu Language: English - Date: 2005-08-13 17:17:45 K-medoids Hierarchical clustering DBSCAN SUBCLU K-means clustering Conceptual clustering Computer cluster Segmentation Consensus clustering Statistics Cluster analysis Fuzzy clustering
17	PDF Document Add to Reading List Source URL: www.dbs.informatik.uni-muenchen.de Language: English - Date: 2004-04-29 03:43:17 SUBCLU Cluster analysis DBSCAN OPTICS algorithm K-means clustering Hans-Peter Kriegel Clustering high-dimensional data Principal component analysis Clique Statistics Data analysis Multivariate statistics
18	PDF Document Add to Reading List Source URL: www.dbs.ifi.lmu.de Language: English - Date: 2008-07-18 10:38:34 Data mining Machine learning ELKI Hans-Peter Kriegel DBSCAN K-means clustering SUBCLU Weka OPTICS algorithm Statistics Cluster analysis Data analysis

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