Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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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.