III-COR: Versatile Co-clustering Analysis for Bi-modal and Multi-modal Data

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Cluster analysis is an indispensable tool in the data miner's arsenal which enables one to understand the structure of the data while conducting exploratory data analysis. Recent times have seen increased occurrences of bi-modal and multi-modal data that manifest themselves as two-dimensional matrices and higher-dimensional tensors. Co-clustering is becoming an increasingly popular technique for exploratory analysis of such data, and has been successfully applied in wide range of areas, including web mining, natural language processing, image and video content analysis, recommender systems, and bioinformatics . The broad goal of this project is to develop sound, theoretical formulations of varied types of co-cluster analyses so that co-cluster analysis becomes an indispensable and efficient tool in the exploratory analysis of bi-modal and multi-modal data. This research focuses on extending co-cluster analysis to include multi-dimensional tensors where one desires to cluster on more than two modes simultaneously, and matrix data with added row and column attributes such as those describing networked knowledge structures or multiple interlinked tables. This will enable co-clustering to reach a much wider class of applications and also make it computationally practical. In order to broaden the impact of this project, the principal investigators are jointly organizing workshops that foster and promote research on various aspects of co-cluster analysis. Data, papers and software developed under this project will be shared with the scientific community via the project Web site (http://hercules.ece.utexas.edu/~ghosh/scalclust.html). Finally, as part of community outreach, the investigators plan to design outreach modules that illustrate data analysis concepts and capabilities at levels appropriate for high school students as well as for freshmen students.