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

Abstract

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. Keywords: Data mining, MLP, Nerve net model, RDISP, Rural development Kırsal Kalkınma Yatırımlarının Desteklenmesi Programı Verileri Kullanılarak Seçilen Algoritmalarının Sınıflandırma Performanslarının Karşılaştırılması ÖzetKapsamlı verileri geleneksel istatistiksel teknikler yardımıyla değerlendirmek mümkün değildir. Bu tür kapsamlı verileri değerlendirmek için "Veri Madenciliği" gibi özel tekniklere ihtiyaç vardır. Veri madenciliği kapsamlı verileri hem sınıflandırarak hem de kümeleyerek değerlendirmeyi kolaylaştırmaktadır. Bu çalışmada, Kırsal Kalkınma Yatırım Destekleme Programı (KKYDP) verilerinde çeşitli kategorize algoritmaları yardımıyla veri madenciliği tekniği kullanılmıştır. Çalışmada en uygun kategorize algoritma mevcut veriler kullanarak belirlenmeye çalışılmıştır. Sonuç olarak; analizlerde en iyi kategorizasyon yapan algoritma modelinin Çok Katmanlı Algılayıcı (ÇKA) yapay sinir ağ modeli olduğu belirlenmiştir.

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