Initializing k-means Clustering

Christian Borgelt; Olha Yarikova

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Paper

Initializing k-means Clustering

Topics: Big Data Search and Mining; Data Analytics; Data Mining; Data Science; Deep Learning; Deep Learning and Big Data; Evolutionary Computing and Optimization; Feature Selection; Fuzzy Computing ; Hybrid Methods; Neural Network Applications; Pattern Recognition; Predictive Modeling; Support Vector Machines

In Proceedings of the 9th International Conference on Data Science, Technology and Applications DATA - Volume 1, 260-267, 2020

Authors: Christian Borgelt ¹ and Olha Yarikova ²

Affiliations: ¹ Paris-Lodron-University of Salzburg, Hellbrunner Straße 34, A-5020 Salzburg, Austria, University of Konstanz, Universitätsstraße 10, D-78457 Konstanz, Germany ; ² University of Konstanz, Universitätsstraße 10, D-78457 Konstanz, Germany

Keyword(s): k-means, Cluster Initialization, Maximin, k-means++.

Abstract: The quality of clustering results obtained with the k-means algorithm depends heavily on the initialization of the cluster centers. Simply sampling centers uniformly at random from the data points usually yields fairly poor and unstable results. Hence several alternatives have been suggested in the past, among which Maximin (Hathaway et al., 2006) and k-means++ (Arthur and Vassilvitskii, 2007) are best known and most widely used. In this paper we explore modifications of these methods that deal with cases, in which the original methods still yield suboptimal choices of the initial cluster centers. Furthermore we present efficient implementations of our new methods.

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Paper citation in several formats:

Borgelt, C. and Yarikova, O. (2020). Initializing k-means Clustering. In Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-440-4; ISSN 2184-285X, SciTePress, pages 260-267. DOI: 10.5220/0009872702600267

@conference{data20,
author={Christian Borgelt. and Olha Yarikova.},
title={Initializing k-means Clustering},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA},
year={2020},
pages={260-267},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009872702600267},
isbn={978-989-758-440-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA
TI - Initializing k-means Clustering
SN - 978-989-758-440-4
IS - 2184-285X
AU - Borgelt, C.
AU - Yarikova, O.
PY - 2020
SP - 260
EP - 267
DO - 10.5220/0009872702600267
PB - SciTePress