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2015 | OriginalPaper | Chapter

Evaluation of Relative Indexes for Multi-objective Clustering

Authors : Tomáš Bartoň, Pavel Kordík

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

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Abstract

One of the biggest challenges in clustering is finding a robust and versatile criterion to evaluate the quality of clustering results. In this paper, we investigate the extent to which unsupervised criteria can be used to obtain clusters highly correlated to external labels. We show that the usefulness of these criteria is data-dependent and for most data sets multiple criteria are required in order to identify the best performing clustering algorithm. We present a multi-objective evolutionary clustering algorithm capable of finding a set of high-quality solutions. For the real world data sets examined the Pareto front can offer better clusterings than simply optimizing a single unsupervised criterion.

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Title: Evaluation of Relative Indexes for Multi-objective Clustering
Authors: Tomáš Bartoň
Pavel Kordík
Publisher: Springer International Publishing
Book: Hybrid Artificial Intelligent Systems
Print ISBN: 978-3-319-19643-5

Electronic ISBN: 978-3-319-19644-2

Copyright Year: 2015
DOI: https://doi.org/10.1007/978-3-319-19644-2_39

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