MultiClust 2014 : SDM 2014 Mini-Symposium on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
Call For Papers
C A L L F O R PA R T I C I P A T I O N
SDM 2014 MINI-SYMPOSIUM on
Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
To be held in conjunction with
The 2014 SIAM International Conference on Data Mining
Philadelphia, Pennsylvania, USA - April 24-26, 2014.
The cross-disciplinary research topic on multiple clustering has received significant attention in recent years. However, since it is relatively young, important research challenges still 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.
The aim of the MultiClust mini-symposium, to be held in conjunction with the 2014 SIAM Data Mining Conference, is to establish a venue for the growing community interested in multiple clustering solutions and in the different research topics related to this field. The mini-symposium will increase the visibility of the topic itself, but also bridge the closely related research areas such as ensemble clustering, co-clustering, clustering with constraints, and frequent pattern mining. In particular, we solicit discussions on approaches for solving emerging problems such as clustering ensembles, semi-supervised clustering, subspace/projected clustering, co-clustering, and multi-view clustering. Of particular interest will be a discussion panel that can draw new and insightful connections between these techniques, and ideas that contribute to the achievement of a unified framework that combines two or more of these techniques.
The target audience consists of researchers and practitioners working on clustering. Besides the researchers directly working on non-redundant clustering, alternative clustering, ensemble clustering, subspace clustering, and clustering with constraints, we will also actively encourage other researchers to attend the mini-symposium.
TOPICS OF INTEREST
● 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 (document mining; health care; privacy and trustworthiness; etc.)
Aarhus University, Denmark
George Mason University, USA
Yahoo! Research, Barcelona, Spain
University of Calabria, Italy
Institut für Informatik, Ludwig-Maximilians-Universität München, Germany