The Unification and Assessment of Multi-Objective Clustering Results of Categorical Datasets with H-Confidence Metric

Onur C. Sert (TOBB Economics and Technology University, Turkey)

Kayhan Dursun (TOBB Economics and Technology University, Turkey)

Tansel Özyer (TOBB Economics and Technology University, Turkey)

Jamal Jida (Lebanese University, Lebanon)

Reda Alhajj (University of Calgary, Canada)

Abstract: Multi objective clustering is one focused area of multi objective optimization. Multi objective optimization attracted many researchers in several areas over a decade. Utilizing multi objective clustering mainly considers multiple objectives simultaneously and results with several natural clustering solutions. Obtained result set suggests different point of views for solving the clustering problem. This paper assumes all potential solutions belong to different experts and in overall; ensemble of solutions finally has been utilized for finding the final natural clustering. We have tested on categorical datasets and compared them against single objective clustering result in terms of purity and distance measure of k-modes clustering. Our clustering results have been assessed to find the most natural clustering. Our results get hold of existing classes decided by human experts.

Keywords: NSGA-II, h-confidence, multi-objective clustering

Categories: I.5.1, I.5.3, I.5.4, I.5.5, J.4