document: Probabilistic modeling of transaction data with applications to profiling , visualization , and prediction Transaction data is ubiquitous in data mining applications . Examples include market basket data in retail commerce , telephone call records in telecommunications , and Web logs of individual page-requests at Web sites . Profiling consists of using historical transaction data on individuals to construct a model of each individual 's behavior . Simple profiling techniques such as histograms do not generalize well from sparse transaction data . In this paper we investigate the application of probabilistic mixture models to automatically generate profiles from large volumes of transaction data . In effect , the mixture model represents each individual 's behavior as a linear combination of `` basis transactions . '' We evaluate several variations of the model on a large retail transaction data set and show that the proposed model provides improved predictive power over simpler histogram-based techniques , as well as being relatively scalable , interpretable , and flexible . In addition we point to applications in outlier detection , customer ranking , interactive visualization , and so forth . The paper concludes by comparing and relating the proposed framework to other transaction-data modeling techniques such as association rules .
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