Affiliations: Département de mathématiques et d'informatique, Université du Québec à Trois-Rivières, Québec, G9A 5H7, Canada | [x] Institute of Mathematics, Russian Academy of Science, Novosibirsk, 630090, Russia | [y] School of Computing and Mathematical Sciences, University of Greenwich, London SE10 9LS, UK
Abstract: Nowadays, decision makers invariably need to use decision support technology (DS) such as data mining (DM) methodologies and tools in order to tackle complex decision making problems. However the successful application of DM technology requires that one possess specific DM decision-making skills. For instance, the effective application of a data mining process is littered with many difficult and technical decisions (i.e. data cleansing, feature transformations, algorithms, parameters, evaluation, etc.) In essence, this contentious problem and burden for decision makers clearly stems from a poor DM-DS integration. As a result, we have strived to improve on this problem by proposing an intelligent DM assistant that can potentially empower decision makers to better leverage DM technology and achieve their intended business objectives. Nonetheless, as this paper will strive to demonstrate, the realization of an intelligent data mining assistant for the decision maker or non-specialist data miner is a challenging and complex endeavour. Hence, in what follows we present the key design considerations (i.e. knowledge representation and reasoning, knowledge elicitation and reuse efforts, etc.) that were addressed during the implementation of a hybrid data mining assistant, based on the case-based reasoning (CBR) paradigm and the use of a formal OWL-DL ontology.
Keywords: Decision support systems, data mining assistants, data mining, knowledge representation, case-based reasoning, formal ontologies, meta-learning
