ES-DM: An Expert System for an Intelligent Exploitation of the Large Data Set
In meeting the challenges that resulted from the explosion of collected, stored, and transferred data, Knowledge Discovery in Databases (KDD) or Data Mining has emerged as an important research area. However, the approaches studied in this area have mainly been oriented at highly structured and precise data. Thus, the problem of exploit these data is often neglected. In this paper, we propose an intelligent approach for exploitation of these data. For this, we propose to define an Expert System (ES) allowing the user to easily exploit the large data set. The Knowledge Base (KB) of our ES is defined by introducing a new KDD approach taking in consideration another degree of granularity into the process of knowledge extraction. This set represents a reduced knowledge of the initial data set and allows deducting the semantics of the data. We prove that, this ES can help the user to give semantics for these data and to exploit them in intelligent way.
KeywordsExpert System Association Rule Fuzzy Cluster Inference Engine Formal Concept Analysis
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