Mining classification rules with Reduced MEPAR-miner Algorithm

KIZILKAYA AYDOĞAN E., GENCER C.

APPLIED MATHEMATICS AND COMPUTATION, cilt.195, sa.2, ss.786-798, 2008 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 195 Sayı: 2
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1016/j.amc.2007.05.024
  • Dergi Adı: APPLIED MATHEMATICS AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.786-798
  • Gazi Üniversitesi Adresli: Evet

Özet

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.