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Mining episode rules in STULONG dataset

This paper is concerned with the STULONG 1 dataset, one of the data collections available for the PKDD Discovery Challenge 2004. This dataset is the result of a twenty-year longitudinal study of risk factors related to atherosclerosis in a population of 1417 middle-aged men. What are the relations between risk factors and clinical demonstration of atherosclerosis? And what are the time intervals over which these relations are valid? To handle such issues, only one approach has been proposed so far in data mining literature: to perform several extractions of sequential patterns in order to test the various possible time intervals. This solution is clearly time consuming, and does not provide any information about reliability of time windows w.r.t. the extracted patterns. In this paper, we propose to mine the STULONG dataset with a new algorithm, WinMiner, that had been recently proposed in [10]. WinMiner provides a single optimized way to find sequential patterns along with their optimal time intervals. Results we obtained are encouraging and provide precious additional information for physicians.