Comparison of the accuracy of classification algorithms on three data-sets in data mining: Example of 20 classes

Abstract

Data mining, which has different uses such as text mining and web mining, is especially used for clustering and classification purposes. In this study, this method was used for both classification and text mining. The aim of the study was the assessment of the performances of the data mining algorithms on the three datasets. A total of 6631 master's and doctoral dissertations written in the field of industrial engineering were downloaded from the Higher Education Council database. With the help of summary, subject titles and keywords of these dissertations, it was tried to be guessed which sub-field of industrial engineering it belongs to using WEKA program. As a result, it was observed that the data set containing the keywords obtained by weighting the expert opinion was more successful than the other two data sets. And the three most successful classification algorithms were found to be kNN, SMO, and J48, respectively.

Keywords: Classification Algorithms, Data Mining, Multiple Classes, Dataset.