Diversity-driven widening

Abstract

This paper follows our earlier publication [1], where we introduced the idea of tuned data mining which draws on parallel resources to improve model accuracy rather than the usual focus on speed-up. In this paper we present a more in-depth analysis of the concept of Widened Data Mining, which aims at reducing the impact of greedy heuristics by exploring more than just one suitable solution at each step. In particular we focus on how diversity considerations can substantially improve results. We again use the greedy algorithm for the set cover problem to demonstrate these effects in practice. © 2013 Springer-Verlag.