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.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1676, pp. 331–342). Springer Verlag. https://doi.org/10.1007/3-540-48298-9_35
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