Generalization Driven Fuzzy Classification Rules Extraction using OLAM Data Cubes

Abstract

An fuzzy classification rules extraction model for online analytical mining (OLAM) was explained in this article. The efficient integration of the concept of data warehousing, online analytical processing (OLAP) and data mining systems converges to OLAM results in an efficient decision support system. Even after associative classification proved as most efficient classification technique there is a lack of associative classification proposals in field of OLAM. While most of existing data cube models claims their superiority over other the fuzzy multidimensional data cubes proved to be more intuitive in user perspective and effectively manage data imprecision. Considering these factors, in this paper we propose an associative classification model which can perform classification over fuzzy data cubes. Our method aimed to improve accuracy and intuitive ness of classification model using fuzzy concepts and hierarchical relations. We also proposed a generalization-based criterion for ranking associative classification rules to improve classifier accuracy. The model accuracy tested on UCI standard  database.