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MultiClust 2011 : ECML/PKDD 2nd MultiClust Workshop on Discovering, Summarizing and Using Multiple Clusterings


When Sep 5, 2011 - Sep 5, 2011
Where Athens, Greece
Submission Deadline Jun 14, 2011
Notification Due Jul 1, 2011
Final Version Due Jul 21, 2011
Categories    machine learning   data mining   clustering

Call For Papers

CFP ECML/PKDD 2nd MultiClust Workshop
on Discovering, Summarizing and Using Multiple Clusterings
in conjunction with the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, Athens, Greece 5-9 Sep 2011

********** Deadline extended to June 14 **********

MultiClust 2011
2nd Workshop on Discovering, Summarizing and Using Multiple Clusterings
will be held in conjunction with ECML/PKDD 2011
5-9 September 2011, Athens, Greece

Following the success of last year’s MultiClust workshop at KDD 2010, we invite submissions to the 2nd MultiClust workshop on discovering, summarizing and using multiple clusterings to be held in conjunction with ECML/PKDD 2011.

Traditionally, clustering has focused on discovering a single summary of the data. In today's applications, however, data is collected for multiple analysis tasks. Several features or measurements provide complex and high dimensional information. In such data, one typically observes several valid groupings, i.e. each data object fits in different roles. In contrast to traditional clustering these alternative clusterings describe multiple aspects that characterize the data in different ways.

The topic of multiple clustering solutions by itself shows multiple research aspects: multiple alternative solutions vs. a single consensus that integrates different views; given views in multi-source clustering vs. detection of novel views by feature selection and space transformation techniques; a virtually unlimited number of alternative solutions vs. a non-redundant output restricted to a small number of disparate clusterings. Further aspects are induced by data representations ranging from traditional continuous valued vector spaces to complex models using graphs, sequences, streams, etc.

The topic of multiple clustering solutions has opened novel challenges in a number of research fields. Examples from the machine learning and knowledge discovery communities include frequent itemset mining, ensemble mining, constraint-based mining, theory on summarization of results, or consensus mining to name only a few. We observe fruitful input from these established related areas. Overall, this cross-disciplinary research endeavor has recently received significant attention from multiple communities. In this workshop, we plan to bring together the researchers from the above research areas to discuss issues in multiple clustering discovery.

Bart Goethals (University of Antwerp, Belgium)
Keynote: "Cartification: from Similarities to Itemset Frequencies"

Michael Houle (National Institute of Informatics, Japan)
Keynote: "Combinatorial Approaches to Clustering and Feature Selection"

The panel at last year's MultiClust workshop and a recent tutorial on discovering multiple clustering solutions document the research interest on this exciting topic. A non-exhaustive list of topics of interest is given below:

* Discovering multiple clustering solutions
o Alternative clusters / disparate clusters / orthogonal clusters
o Multi-view clustering / subspace clustering / co-clustering
o Multi-source clustering / clustering in parallel universes
o Feature selection and space transformation techniques
o Constraint-based mining for the detection of alternatives
o Non-redundant view detection and non-redundant cluster detection
o Model selection problem: how many clusterings / how many clusters
o Iterative vs. simultaneous processing of multiple views
o Scalability to large and high dimensional databases
o Tackling complex databases (e.g. graphs, sequences, or streams)
* Summarizing multiple clustering solutions
o Ensemble techniques
o Meta clustering
o Consensus mining
o Summarization and compression theory
* Using and evaluating multiple clustering solutions
o Classification based on multiple clusterings
o Evaluation metrics for multiple clustering solutions
o Visualization and exploration of multiple clusterings
* Related research fields
o Frequent itemset mining
o Subgroup mining
o Subspace learning
o Relational data mining
o Transfer mining
* Applications of multiple clustering solutions
o Bioinformatics: gene expression analysis / proteomics / ...
o Sensor network analysis
o Social network analysis
o Health surveillance
o Customer segmentation
o ... and many more ...

We encourage submissions describing innovative work in other, related, fields that address the issue of multiplicity in data mining.

We invite submission of unpublished original research papers that are not under review elsewhere. All papers will be peer reviewed. Papers may be up to 12 pages long. We also invite vision papers and descriptions of work-in-progress or case studies on benchmark data as short paper submissions of up to 6 pages. If accepted, at least one of the authors must attend the workshop to present the work.

Contributions should be submitted in pdf format using the workshop’s EasyChair submission site at The submitted papers must be written in English and formatted according to the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines. Author's instructions and style files can be downloaded at:

If you are considering submitting to the workshop and have questions regarding the workshop scope or need further information, please do not hesitate to contact the PC chairs.

We will edit on-line proceedings of all accepted papers so that the results are widely accessible. Proceedings will be published through the CEUR Workshop Proceedings ( publication service in time for the workshop. If there is sufficient interest and quality of papers, we will also consider a post-workshop publication (e.g., as a special issue in a journal).

Among the accepted papers, a best paper award carrying the value of 300 EURO will be granted to innovative contributions in the new field of multiple clusterings.

A best benchmarking dataset award carrying the value of 300 Euro will be granted. Therefore, all submissions are encouraged to provide their experiment data publicly available. Among the accepted papers reviewers will select one paper with the most valuable benchmark dataset for the community. Benchmark data should stem from a real world application, contain well-documented ground truth information on diverse aspects of the data, and be released to the general public. A collection of all participating datasets will be made available on the workshop website.

Submission deadline: June 14, 2011 *** extended ***
Acceptance notification: July 1, 2011
Camera-ready deadline: July 21, 2011

Emmanuel Müller, Karlsruhe Institute of Technology (KIT), Germany
Stephan Günnemann, RWTH Aachen University, Germany
Ira Assent, Aarhus University, Denmark
Thomas Seidl, RWTH Aachen University, Germany

James Bailey (University of Melbourne, Australia)
Carlotta Domeniconi (George Mason University, USA)
Ines Färber (RWTH Aachen University, Germany)
Vivekanand Gopalkrishnan (Nanyang Technological University, Singapore)
Dimitrios Gunopulos (University of Athens, Greece)
Michael Houle (National Institute of Informatics, Japan)
Daniel Keim (University of Konstanz, Germany)
Themis Palpanas (University of Trento, Italy)
Magda Procopiuc (AT&T Research, USA)
Naren Ramakrishnan (Virginia Tech, USA)
Jörg Sander (University of Alberta, Canada)
Alexander Topchy (Nielsen Media Research)
Lyle H. Ungar (University of Pennsylvania, USA)
Jilles Vreeken (University of Antwerp, Belgium)
Wei Wang (University of North Carolina at Chapel Hill, USA)
Arthur Zimek (University of Munich, Germany)

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