Open AccessUnsupervised Discretization of Continuous Variables in a Chicken Egg Quality Traits Dataset
Author(s) -
Zeynel Cebecí,
Figen Yıldız
Publication year - 2017
Publication title -
türk tarım - gıda bilim ve teknoloji dergisi
Language(s) - English
Resource type - Journals
ISSN - 2148-127X
DOI - 10.24925/turjaf.v5i4.315-320.1056
Subject(s) - discretization , cluster analysis , statistics , tree (set theory) , pattern recognition (psychology) , computer science , mathematics , random forest , artificial intelligence , data mining , mathematical analysis
Discretization is a data pre-processing task transforming continuous variables into discrete ones in order to apply some data mining algorithms such as association rules extraction and classification trees. In this study we empirically compared the performances of equal width intervals (EWI), equal frequency intervals (EFI) and K-means clustering (KMC) methods to discretize 14 continuous variables in a chicken egg quality traits dataset. We revealed that these unsupervised discretization methods can decrease the training error rates and increase the test accuracies of the classification tree models. By comparing the training errors and test accuracies of the model applied with C5.0 classification tree algorithm we also found that EWI, EFI and KMC methods produced the more or less similar results. Among the rules used for estimating the number of intervals, the Rice rule gave the best result with EWI but not with EFI. It was also found that Freedman-Diaconis rule with EFI and Doane rule with EFI and EWI slightly performed better than the other rules.