A Clustering Accuracy Comparison Framework - Kwale, F. M.; Wagacha, P. W.; Kahonge, A. M. (African Journal of Education,Science and Technology (AJEST), 2020)
Clustering is a data mining problem of dividing documents into groups, such that documents in one group are more similar than those in other groups. The aim of this study is to propose a framework for comparing the accuracy of clustering algorithms. The study applies qualitative research through document analysis to review previous clustering algorithms’ comparisons so as to obtain the issues/problems with such previous comparisons. We then deduce appropriate comparisons framework that addresses the problems. The study obtained the following comparison issues: Nature of comparison, nature of data, size of data, source of data, evaluation metrics, and parameter settings. Consequently, the study proposed the rules, formulae, and procedures needed to be used in a comparison. It is recommended that applying this framework will ensure that such evaluations and comparisons are done using formal procedures that will yield dependable results. This study suggests a further study to be done to apply this framework and do a comprehensive comparison of some clustering algorithms.