A Pareto-Dominant Clustering Approach for Pareto-Frontiers.
Johannes Kästner, Markus Endres, Werner Kießling · OPUS (Augsburg University) · 2017
anaging large and confusing sets of increasing data is a well-known problem in Data Mining. Since compromises in many use cases like Recommender Systems or preference-based applications are becoming more and more usual, it is very useful to cluster sets of promising results in order to get an overview and present them properly. In this paper we present the Pareto-dominance as a very suitable and promising approach to cluster objects over better than relationships. In order to meet someones desires, one can tip the balance of the final results to the more favored dimension if no decision for allocating objects is possible.