A Guided Tour through the Data Mining Jungle

Robert Engels, University of Karlsruhe, Germany; Guido Lindner, Daimler Benz AG, Germany; Rudi Studer, University of Karlsruhe, Germany

An important success factor for the field of KDD lies in the development and integration of methods for supporting the construction and execution of KDD processes. Crucial aspects in this context are the (incremental) development of a precise problem description, a decomposition of this top level problem description into manageable and compatible subtasks which can be reused, and a selection and combination of adequate algorithms for solving these subtasks. In this paper we describe an approach for supporting the systematic decomposition of a KDD process into subtasks and for selecting appropriate problem-solving methods and algorithms for solving these subtasks. Our approach has been partially integrated into the CLEMENTINE system and has been used to develop a real world application in the area of prediction.

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