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Open AccessArticle

Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering

Faculty of Information Technology, University of Jyvaskyla, P.O. Box 35, FI-40014 Jyvaskyla, Finland
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Algorithms 2017, 10(3), 105;
Received: 13 July 2017 / Revised: 28 August 2017 / Accepted: 1 September 2017 / Published: 6 September 2017
(This article belongs to the Special Issue Clustering Algorithms 2017)
PDF [516 KB, uploaded 6 September 2017]


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. View Full-Text
Keywords: prototype-based clustering; clustering validation index; robust statistics prototype-based clustering; clustering validation index; robust statistics

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Hämäläinen, J.; Jauhiainen, S.; Kärkkäinen, T. Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering. Algorithms 2017, 10, 105.

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