Generating Fuzzy Summaries: a New Approach based on Fuzzy Multidimensional Databases
Résumé
Intelligent data analysis faces the problem of the huge amounts of data. More and more, database management systems are required to deal with this large repositories. In this framework, multidimensional databases are particularly adapted. They have emerged to support the OLAP framework. OLAP, standing for On Line Analytical Processing, is devoted to the fast analysis of multidimensional data. This model has been recently extended to the treatment of imperfect data and flexible queries. In this paper, we propose a new architecture based on fuzzy multidimensional databases to generate fuzzy summaries. This approach offers two main advantages. First, it provides a scalable framework due to the use of a database management system. Second, the introduction of fuzziness provides a theoretical framework to handle data from the real world and flexible queries. The chosen data mining tool is the generation of linguistic summaries. This kind of rules is a more understandable knowledge for the user than classical association rules. A user-friendly system is provided. This approach is compared to existing frameworks devoted to data analysis with association rules or fuzzy summaries. We insist on the fact that this model generalizes the classical one. It provides a framework to handle all classical crisp cases, since fuzzy set theory provides means to handle imperfect and classical data. Thus this method may be applied on classical data to generate fuzzy summaries.