An empirical comparison of machine learning clustering methods in the study of Internet addiction among students majoring in Computer Sciences

Abstract

One of the relevant current vectors of study in machine learning is the analysis of the application peculiarities for methods of solving a specific problem. We will study this issue on the example of methods of solving the clustering problem. Currently, we have a considerable number of learning algorithms which can be used for clustering. However, not all methods can be used for solving a specific task. The article describes the technology of empirical comparison of methods of clustering problem solving using WEKA free software for machine learning. Empirical comparison of data clustering methods was based on the results of a survey conducted among students majoring in Computer Sciences and dedicated to detecting signs of Internet addiction (IA) as behavioural disorder that occurs due to Internet misuse. Empirical comparison of Expectation Maximization, Farthest First and K-Means clustering algorithms together with the application of the WEKA machine learning system had the following results. It described the peculiarities of application of these methods in feature clustering. The authors developed data instances’ clustering models to detect signs of Internet addiction among students majoring in Computer Sciences. The study concludes that these methods may be applicable to development of models detecting respondent groups with signs of IA related disorders.