Mining classification rules with Reduced MEPAR-miner Algorithm

In this study, a new classification technique based on rough set theory and MEPAR-miner algorithm for association rule mining is introduced. Proposed method is called as `Reduced MEPAR-miner Algorithm'. In the method being improved rough sets are used in the preprocessing stage in order to reduce the dimensionality of the feature space and improved MEPAR-miner algorithms are then used to extract the classification rules. Besides, a new and an effective default class structure is also defined in this proposed method. Integrating rough set theory and improved MEPAR-miner algorithm, an effective rule mining structure is acquired. The effectiveness of our approach is tested on eight publicly available binary and n-ary classification data sets. Comprehensive experiments are performed to demonstrate that Reduced MEPAR-miner Algorithm can discover effective classification rules which are as good as (or better) the other classification algorithms. These promising results show that the ro...

• Type: Journal
• Year: 2008
• Where: AMC
• Authors: Emel Kizilkaya Aydogan, Cevriye Gencer
• Keywords: AMC 2008, Mepar-miner Algorithm, Rough Set, Rough Set Theory