The Use of Computational Creativity Metrics to Evaluate Alternative Values for Clustering Algorithm Parameters

  • Andrés Gómez de Silva GarzaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)


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.


Computational creativity Clustering k-means 



This work has been supported by Asociación Mexicana de Cultura, A.C. This work was done while the author was on sabbatical leave at the Computational Intelligence Laboratory (CIL), School of Computer Engineering, Nanyang Technological University, Singapore. The author gratefully acknowledges the opportunity enabled by Professor Erik Cambria to do research there. While at the CIL the author benefitted from discussions with Anupam Mondal and Ranjan Satapathy, who also provided and processed the medical domain data and produced the graphs shown in Figs. 2 and 3. Many thanks are also due to Daniel Espinosa Mireles de Villafranca at ITAM, who helped to polish the final version of this paper.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringITAMMexico CityMexico

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