Mining classification rules with Reduced MEPAR-miner Algorithm

Emel Kızılkaya Aydoğan

doi:10.1016/j.amc.2007.05.024

2008-02-01

PRE

Emel Kızılkaya Aydoğan

Cevriye Gencer *

DOI：10.1016/j.amc.2007.05.024

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En 中文

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 rough set approach is a useful tool for preprocessing of data for improved MEPAR-miner algorithm. (c) 2007 Elsevier Inc. All rights reserved.

Keyword:

data mining

classification rules

attribute reduction

rough set

evolutionary programming