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On the Evaluation of Mined Frequent Sequences - An Evidence Theory-based Method

Frequent sequences (or temporal associations) mining is a very important topic within the temporal data mining area. Syntactic simplicity, combined with the dual characteristics (descriptive and predictive) of the mined temporal patterns, allow the extraction of useful knowledge from dynamic domains, which are timevarying in nature. Some of the most representative algorithms for mining sequential patterns or frequent associations are Apriori-like algorithms and, therefore, they cannot handle numeric attributes or items. This peculiarity makes it necessary to add a new process in the data preparation step, the discretization process. An important fact is that, depending on the discretization technique used, the number and type of discovered temporal patterns change dramatically. In this paper, we propose a method based on the Shafer’s Theory of Evidencethat uses two information measures proposed by Yagerfor the quality evaluation of the extracted sets of temporal patterns. From a practical point of view, the main goal is to select, for a given dataset, the best discretization technique that leads to the discovery of useful knowledge. Nevertheless the underlying idea is to propose a formal method for assessing the mined patterns, seen as a belief structure, in terms of certainty in the information that represents. In this work, we also present a practical example, describing an application of this proposal in the Intensive Care Burn Unit domain.