Classify high dimensional datasets using discriminant positive negative association rules

The purpose of this paper is to investigate the ability of binary classification using the possitive negative association rules mining (PNARs) for large dataset. The PNARs (as understood until now) called narrow PNARs (NPNARs for short) as well as their generalized forms called expanded PNARs (EPNARs for short) will be investigated. The paper proposes a classification algorithm based on mining the discriminant NPNARs and EPNARs. The algorithm integrates Dimensionality Reduction, Rule Generation, Removal of redundant rules and Classification. The number of classified instances in testing datasets and the classification performance using the valid disriminant NPNARs and EPNARs found out by the proposed algorithm are compared with each other and with 12 other classification algorithms.Experimental results show that the mining of EPNARs rather than that of NPNARs has prospects to be a classification technique applied in the real world. The paper also outlines some issues that need to be researched in the near future so that using the mining of the discriminant EPNARs becomes an efficient technique for classifying high dimensional datasets.