Spectral Analysis for Data Mining

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This research is concerned with the development, analysis and empirical evaluation of algorithms for information retrieval and data mining, primarily using spectral analysis. A partial list of the fundamental questionsbeing addressed are: How should data be stored,organized and processed so as to allow the most effective retrieval ofinformation? How can ``important'' structure and ``meaningful''patterns be found within a large data-set? How and when can thishidden structure be used to facilitate determination of missing dataor to ``clean'' data that is imprecise or partially incorrect?What are appropriate models for data generation, and how canthese models be used to improve the design of data mining algorithms?The researchers are studying applications that have already received a great deal of attention and on whichempirical success has been achieved, including objectclustering and web site ranking. In addition, they are designing and analyzing algorithms, and developing and analyzing models for newer data mining problems, including collaborative filtering, topic distillation, spam detection and prevention, and hierarchicalclustering. Matrix perturbation theory is the foundation for the development of theoretical results. The researchers are also studyingnew techniques for speeding up the computation of the SVD.The theoretical research is being complemented byexperimental evaluation on real data sets.