Abstract
Probabilistic mixture models have been used in statistics for well over a century as flexible data models. More recently these techniques have been adopted by the machine learning and data mining communities in a variety of application settings. We begin this talk with a review of the basic concepts of finite mixture models: what can they represent? how can we learn them from data? and soon. We will then discuss how the traditional mixture model (defined in a fixed dimensional vector space) can be usefully generalized to model non-vector data, such as sets of sequences and sets of curves. A number of real-world applications will be used to illustrate how these techniques can be applied to large-scale real-world data exploration and prediction problems, including clustering of visitors to a Web site based on their sequences of page requests, modeling of sparse high-dimensional |ldmarket basket” data for retail forecasting, and clustering of storm trajectories in atmospheric science.
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© 2002 Springer-Verlag Berlin Heidelberg
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Smyth, P. (2002). Learning with Mixture Models: Concepts and Applications. In: Elomaa, T., Mannila, H., Toivonen, H. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2002. Lecture Notes in Computer Science, vol 2431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45681-3_43
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DOI: https://doi.org/10.1007/3-540-45681-3_43
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