Comparison of Classification Performance of Selected Algorithms Using Rural Development Investments Support Programme Data

ALAN M. A., Yesilyurt C., Aydin S., Aydin E.

KAFKAS UNIVERSITESI VETERINER FAKULTESI DERGISI, cilt.20, sa.3, ss.351-356, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 3
  • Basım Tarihi: 2014
  • Doi Numarası: 10.9775/kvfd.2013.10154
  • Dergi Adı: KAFKAS UNIVERSITESI VETERINER FAKULTESI DERGISI
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.351-356
  • Anahtar Kelimeler: Data mining, MLP, Nerve net model, RDISP, Rural development, DATA MINING TECHNIQUES, MANAGEMENT
  • Atatürk Üniversitesi Adresli: Hayır

Özet

It is not always possible to solve a large size of data via traditional statistical techniques. In order to solve these kinds of data special tactics like data mining are needed. Data mining may meet these kinds of needs with both categorizing and piling tactic. In this study, we have used data mining by using Rural Development Investment Support Program (RDISP) data with various categorizing algorithms. The most prospering categorizing algorithm was tried to determine by using present data. At the end of analysis, it has been understood that MLP (multilayer perceptron), a nerve net model, is the best algorithm that makes the best categorizing.