Towards Fuzzy-OLAP Mining

Multidimensional databases and OLAP (On Line Analytical Processing) tools provide an eÆcient framework for data mining and have led to the so-called OLAP Mining architecture ([10]). Besides, data from real world are often imperfect, either because they are uncertain, or because they are imprecise. Moreover, the use of fuzzy set theory in data mining systems enhances the understandability of the discovered knowledge when considering numerical attributes and leads to more generalizable rules. This is the reason why we have proposed an approach to perform OLAP-based mining using multidimensional databases and fuzzy decision trees ([17]). In recent works, an extension of multidimensional databases has been de ned in order to handle imperfect information and exible multidimensional queries ([15]). In this paper, we show that this model is well adapted for data mining. A general architecture is proposed, which uses fuzzy multidimensional databases as a support for knowledge discovery.