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Emmanuel Müller
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Publications
- 2015
- [j8]Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl, Jennifer G. Dy:
MultiClust special issue on discovering, summarizing and using multiple clusterings. Mach. Learn. 98(1-2): 1-5 (2015) - 2012
- [c28]Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDE 2012: 1207-1210 - 2011
- [c24]Emmanuel Müller, Ira Assent, Stephan Günnemann, Patrick Gerwert, Matthias Hannen, Timm Jansen, Thomas Seidl:
A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. BTW 2011: 347-366 - [c23]Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl:
Scalable density-based subspace clustering. CIKM 2011: 1077-1086 - [c22]Stephan Günnemann, Ines Färber, Emmanuel Müller, Ira Assent, Thomas Seidl:
External evaluation measures for subspace clustering. CIKM 2011: 1363-1372 - [c20]Stephan Günnemann, Emmanuel Müller, Sebastian Raubach, Thomas Seidl:
Flexible Fault Tolerant Subspace Clustering for Data with Missing Values. ICDM 2011: 231-240 - [e1]Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl:
Proceedings of the 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings, Athens, Greece, September 5, 2011, in conjunction with ECML/PKDD 2011. CEUR Workshop Proceedings 772, CEUR-WS.org 2011 [contents] - 2010
- [c16]Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDM 2010: 1220 - 2009
- [j3]Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl:
Evaluating Clustering in Subspace Projections of High Dimensional Data. Proc. VLDB Endow. 2(1): 1270-1281 (2009) - [c14]Stephan Günnemann, Emmanuel Müller, Ines Färber, Thomas Seidl:
Detection of orthogonal concepts in subspaces of high dimensional data. CIKM 2009: 1317-1326 - [c13]Emmanuel Müller, Ira Assent, Stephan Günnemann, Ralph Krieger, Thomas Seidl:
Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data. ICDM 2009: 377-386 - [c11]Emmanuel Müller, Ira Assent, Ralph Krieger, Stephan Günnemann, Thomas Seidl:
DensEst: Density Estimation for Data Mining in High Dimensional Spaces. SDM 2009: 175-186
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