Mineração de padrões temporais híbridos especificados na lógica temporal de intervalos

Abstract

Discovering frequent patterns in databases is an important problem for knowledge discovery and its importance is justified by the diversity of areas where it can be used, such as retail, financial market, medicine, agriculture, farming, telecommunications, etc. The problem of mining frequent patterns in temporal databases, also know as mining temporal patterns, has been widely studied. In some works the temporal patterns are expressed by propositional temporal logic, and in others, the temporal patterns are more expressive and are expressed by first order temporal logic. The existing temporal patterns in the literature represent the time either in terms of points, where event occur in determined instants, or in terms of intervals, where event occur during a period of time. This dichotomy implies that some facts may not be inferred. For instance, facts related to the clinical history of a patient, where, the patient took some medicine during a period of time and was submitted to a surgery on a day, during the period when he or she was taking the medicine. In this dissertation, we propose a new temporal pattern, called the hybrid temporal pattern or simply htp, where, time is represented in terms of points and/or intervals. This pattern is very expressive. We adapted Allen's Interval Temporal Logic to specify it. We also proposed the algorithm MILPRIT* for mining the frequents htp's in a database with respect to a minimum support and satisfying a constraint specified by user through a regular expression. Doing so, we allow the user to control the process of htp discovery. The performance and scalability of MILPRIT* has been evaluated through a set of experiments over synthetic and real databases.