Scalable Clustering of Complex Data

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This research addresses three key issues pertaining to the clustering of large, complex datasets. First, a unifying view of model-based clustering will be developed to form a theoretical basis for understanding and comparing a wide range of existing clustering algorithms for complex data. This view will be systematically explored to develop improved algorithms for specific applications. Second, complexity arising from domain constraints on balancing and dealing with incrementally acquired non-stationary data, such as newsfeeds, is addressed via adaptive clustering techniques. Finally, methods for obtaining a single consensus solution given multiple clustering results are investigated. Such methods will facilitate distributed data mining under severe restrictions on data sharing due to privacy and other constraints. Benchmarks for the proposed research areas will be developed and made available to the research community. Further information about the project is available on the project web site http://www.lans.ece.utexas.edu/scalclust.html. Broader impacts of this project also include outreach activities to high school students and freshmen students. Demonstration modules that illustrate data analysis issues will be designed for students and enable them to use case studies resulting from this work and gain understanding of clustering techniques.