K-Ortalama Kümelerinin Sınıf Bilgisi Olarak Karar Ağacı Oluşturmada Kullanılması ve Glokom Çoklu Sınıflandırılmasında Başarıma Etkisi

Year 2016, Volume: 4 Issue: 2, 747 - 755, 11.03.2016

Sait Can Yücebaş Ahmet Cumhur Kınacı

Abstract

Bu çalışma çoklu sınıflandırmada performans artırımı için K-Ortalama ve Karar Ağacı yöntemlerinden oluşan bir model sunmaktadır. Model glukom veri kümesi üzerinde test edilmiş  kesinlik ölçütü 0,808, ROC alanı 0,839 bulunmuştur.

Keywords

Veri Madencliği, Sınıflama, Kümeleme, K-Ortalama, Karar Ağacı

Usage Of K-Means Clusters as Class Labes In Decısıon Trees and Its Effect On Multıclassıfıcatıon Performance Of Glaucoma

Abstract

In this study a model of K-Means - Decision Tree is presented to increase the multiclassification performance. This model is tested on glaucoma dataset, the accuracy and the are under ROC curve is calculated as 0.808, 0.839 respectively.

Keywords

Data Mining, Classification, Clustering, K-Means, Decision Tree

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