Multidimensional Clustering Approaches for Pareto-frontiers

J. Kastner, M. Endres

erschienen 06.06.2017 Technical Report, Institute of Computer Science, University of Augsburg, June 2017

Abstract: In Data Mining large and increasing sets of data are becoming more and more common. In order to avoid losing the overview on these data-sets, preference queries are a very popular method to reduce quantities of data to high relevant information. Together with clustering methods like k-means, confusing sets of objects can be constituted and presented clearer in order to get a better overview. In this report we present on the one hand 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. On the other hand we introduce based on the Pareto-dominance an advanced clustering approach exploiting the Borda Social Choice voting rule to manage distances of different domains by equally weights during the clustering process.