Unsupervised method to ensemble results of multiple clustering solutions for bibliographic data

Abstract

Multiobjective optimization refers to optimization of multiple conflicting objective functions simultaneously. Clustering problem is often formulated as a multiobjective optimization problem where multiple cluster quality measures are simultaneously optimized and Pareto based approaches are popular in solving that Pareto based approaches yield a set of solutions known as Pareto front where all the solutions are non-dominated with respect to each other. A single solution is selected by the decision maker according to his/her preference. But when the number of non-dominated solutions is large in number, then it is difficult for the decision maker to choose the one solution. The selection of a solution from the given Pareto front is known as Post-Pareto optimality analysis. In the past many approaches were proposed for solving the aforementioned problem, but most of these involve the decision maker. In this paper, we have proposed an approach to obtain a single solution from a set of non-dominated solutions by combining these solutions without the intervention of the decision maker. We have evaluated our approach on the set of solutions obtained after application of a newly developed multiobjective based clustering technique on bibliographic databases like DBLR.