A Complexity Guided Algorithm for Association Rule Extraction on Fuzzy DataCubes

The use of online analytical processing (OLAP) systems as data sources for data mining techniques has been widely studied and has resulted in what is known as online analytical mining (OLAM). As a result of both the use of OLAP technology in new fields of knowledge and the merging of data from different sources, it has become necessary for models to support imprecision. We, therefore, need OLAM methods which are able to deal with this imprecision. Association rules are one of the most used data mining techniques. There are several proposals that enable the extraction of association rules on DataCubes but few of these deal with imprecision in the process. The main problem observed in these proposals is the complexity of the rule set obtained. In this paper, we present a novel association rule extraction method that works over a fuzzy multidimensional model which is capable of representing and managing imprecise data. Our method deals with the problem of reducing the complexity of the result obtained by using fuzzy concepts and a hierarchical relation between them.