A Multiple Aspect Data Model Design for Knowledge Discovery in Databases

M. Sassi(1*), A. Grissa Touzi(2), H. Ounelli(3)

(1) National School of Engineering of Tunis, Tunisia
(2) Department of Technologies of Information and Communications in the National School of Engineering of Tunis, Tunisia
(3) Computer sciences Department of Faculty of Sciences of Tunis, Tunisia
(*) Corresponding author

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The unifying goal of the KDD process is to extract knowledge from data in the context of large databases. Data mining lies at the heart of the process. It makes it possible to extract information from the data. There are a great many different methods of data mining (classification, decision trees, neural networks, genetic algorithms, Formal Concept Analysis etc.). The choice of which methods to use depends on the purpose of the building model. It can have one of the following aspects: organizational or hierarchical. In this article, we present a model design joining both the two aspects and permit to the user a multiple choices of deployment.
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Data Mining; Organizational Model; Clustering; Hierarchical Model; FCA; Fuzzy Logic

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