Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering
Hämäläinen, J., Jauhiainen, S., & Kärkkäinen, T. (2017). Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering. Algorithms, 10(3), Article 105. https://doi.org/10.3390/a10030105
© 2017 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estimate this number without any external information. The purpose of this article is to evaluate, empirically, characteristics of a representative set of internal clustering validation indices with many datasets. The prototype-based clustering framework includes multiple, classical and robust, statistical estimates of cluster location so that the overall setting of the paper is novel. General observations on the quality of validation indices and on the behavior of different variants of clustering algorithms will be given.
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Except where otherwise noted, this item's license is described as © 2017 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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