Séminaire Donnees et APprentissage Artificiel
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From mining under constraints to mining with constraints
Intervenant(s) : Ahmet Samet (IRISA)The mining of frequent itemsets from uncertain databases has become a very hot topic within the data mining community over the last few years. Although the extraction process within binary databases constitutes a deterministic problem, the uncertain case is based on expectation. Recently, a new type of databases also referred as evidential database that handle the constraint of having both uncertain and imprecise data has emerged. In this talk, we present an applicative study case of evidential databases use within the chemistry field. Then, we shed light on a WEvAC approach for amphiphile molecule properties prediction.
Furthermore, the most existing approaches of pattern mining, which are based on procedural programs (as we often use/develop), would require specific and long developments to support the addition of extra constraints. In view of this lack of flexibility, such systems are not suitable for experts to analyze their data. Recent researches on pattern mining have suggested to use declarative paradigms such as SAT, ASP or CP to provide more flexible tools for pattern mining. The ASP framework has been proven to be a good candidate for developing flexible pattern mining tools. It provides a simple and principled way for incorporating expert's constraints within programs.
Ahmed Samet is a post-doctoral researcher at the University of Rennes 1. He received his M.Sc. degree in Computer Science from the Université de Tunis (Tunisia) in 2010. Then, he obtained a Ph.D. in Computer Science within a Cotutelle agreement between the Université de Tunis (Tunisia) and Université d'Artois (France). He held, at first, the position of a postdoctoral researcher with Sorbonne University: Université de technologie de Compiegne (France). His research topics involve decision making, machine learning under uncertainty and data mining.
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