The use of computational creativity metrics to evaluate alternative values for clustering algorithm parameters

Abstract

This paper presents some details on how to use concepts from computational creativity to inform the machine learning task of clustering. Specifically, clustering involves structuring exemplar-based knowledge. The novelty and usefulness of the way the knowledge ends up being structured can be measured. These are characteristics that traditionally computational creativity focuses on whereas machine learning doesn’t, but they can aid in selecting the best value for the parameters of the learning task. Doing so also provides us with a way to find an adequate balance between novelty and usefulness, something that still hasn’t been fully formalized in computational creativity. Thus both fields, machine learning and computational creativity, can benefit from this type of hybrid research.