MultiClust 2013 - 4th Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering

4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering in conjunction with the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA, August 11-14 2013.
Following the success of the MultiClust workshops at KDD 2010, ECML PKDD 2011, SDM 2012, as well as the success of the 3Clust workshop at PAKDD 2012, we invite submissions to the 4th MultiClust workshop on multiple clusterings, multi-view data, and multi-source knowledge-driven clustering to be held in conjunction with SIGKDD 2013.
Multiple views and data sources require clustering techniques capable of providing several distinct analyses of the data. The cross-disciplinary research topic on multiple clustering has thus received significant attention in recent years. However, since it is relatively young, important research challenges remain. Specifically, we observe an emerging interest in discovering multiple clustering solutions from very high dimensional and complex databases. Detecting alternatives while avoiding redundancy is a key challenge for multiple clustering solutions. Toward this goal, important research issues include: how to define redundancy among clusterings; whether existing algorithms can be modified to accommodate the finding of multiple solutions; how many solutions should be extracted; how to select among far too many possible solutions; how to evaluate and visualize results; how to most effectively help the data analysts in finding what they are looking for. Recent work tackles this problem by looking for non-redundant, alternative, disparate or orthogonal clusterings. Research in this area benefits from well-established related areas, such as ensemble clustering, constraint-based clustering, frequent pattern mining, theory on result summarization, consensus mining, and general techniques coping with complex and high dimensional databases. At the same time, the topic of multiple clustering solutions has opened novel challenges in these research fields.
Overall, this cross-disciplinary research endeavor has recently received significant attention from multiple communities. In this workshop, we plan to bring together researchers from the above research areas to discuss issues in multiple clustering discovery. We solicit approaches for solving emerging issues in the areas of clustering ensembles, semi-supervised clustering, subspace/projected clustering, co-clustering, and multi-view clustering. Of particular interest will be papers that draw new and insightful connections between these areas, and papers that contribute to the achievement of a unified framework that combines two or more of these problems.
TOPICS OF INTEREST
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The panel discussions at the last MultiClust workshops 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:
? Clustering Ensembles
? Co-clustering Ensembles
? Subspace/Projected Clustering
? Semi-supervised Clustering
? Multiview / Alternative Clustering
? Handling Redundancy in Clustering Results
? Bayesian Learning for Clustering
? Model Selection Issues: How Many Clusters?
? Co-clustering with External Knowledge for Relational Learning
? Probabilistic Clustering with Constraints
? Kernels for Semi-supervised Clustering
? Active Learning of Constraints in Clustering Ensembles
? Constraint-based Clustering for Uncertain Data Management and Mining
? Integration of Frequent Pattern Mining in (Semi-supervised) Multi-view Clustering
? Evaluation Criteria for Multi-view Data Clustering
? Benchmark Data for Multi-view Data Clustering
? Incorporating User Feedback in Semi-supervised Clustering
? Clustering Ensembles for Uncertain Data Management and Mining
? Multiple clusterings and multi-view data in Heterogeneous Information Networks
? Applications (e.g. document mining, health care, privacy and trustworthiness)
We encourage submissions describing innovative work in related fields that address the issue of multiplicity in data mining.
SUBMISSION GUIDELINES
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Information on submission and proceedings will be announced soon.
IMPORTANT DATES
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TBA
INVITED SPEAKERS
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Shai Ben-David, University of Waterloo, Canada
Michael Berthold, University of Konstanz, Germany
PROGRAM CHAIRS
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Ira Assent, Aarhus University, Denmark
Carlotta Domeniconi, George Mason University, USA
Francesco Gullo, Yahoo! Research, Spain
Andrea Tagarelli, University of Calabria, Italy
Arthur Zimek, Ludwig-Maximilians-Universität München, Germany
PROGRAM COMMITTEE (so far)
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James Bailey, University of Melbourne, Australia
Ricardo J. G. B. Campello, University of São Paulo, Brazil
Xuan-Hong Dang, Aarhus University, Denmark
Ines Färber, RWTH Aachen University, Germany
Wei Fan, IBM T. J. Watson Research Center and IBM CRL, USA
Ana Fred, Technical University of Lisbon, Portugal
Stephan Günnemann, CMU, USA
Dimitrios Gunopulos, University of Athens, Greece
Michael E. Houle, NII, Japan
Emmanuel Müller, KIT, Germany
Erich Schubert, LMU Munich, Germany
Grigorios Tsoumakas, Aristotle University of Thessaloniki (AUTh), Greece
Giorgio Valentini, University of Milan, Italy
Jilles Vreeken, University of Antwerp, Belgium