Abstract
A new modeling technique to mine information from data that are expressed in the form of events associated to entities is presented. In particular such a technique aims at extracting non-evident behavioral patterns from data in order to identify different classes of entities in the considered population. To represent the behavior of the entities a Markov chain model is adopted and the transition probabilities for such a model are computed. The information extracted by means of the proposed technique can be used as decisional support in a large range of problems, such as marketing or social behavioral questions. A case study concerning the university dropout problem is presented together with further development of Markov chain modeling technique in order to improve the prediction and/or interpretation power.
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© 1999 Springer-Verlag Berlin Heidelberg
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Massa, S., Paolucci, M., Puliafito, P.P. (1999). A New Modeling Technique Based on Markov Chains to Mine Behavioral Patterns in Event Based Time Series. In: Mohania, M., Tjoa, A.M. (eds) DataWarehousing and Knowledge Discovery. DaWaK 1999. Lecture Notes in Computer Science, vol 1676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48298-9_35
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DOI: https://doi.org/10.1007/3-540-48298-9_35
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