A new algorithm for choosing initial cluster centers for k-means

DOI

10.2991/iccsee.2013.135How to use a DOI?

Keywords

K-means, cluster analysis

Abstract

The k-means algorithm is widely used in many applications due to its simplicity and fast speed. However, its result is very sensitive to the initialization step: choosing initial cluster centers. Different initialization algorithms may lead to different clustering results and may also affect the convergence of the method. In this paper, we propose a new algorithm for improving the initialization of the cluster centers by reducing dimensions followed by moving cluster centers towards high density regions. Our algorithm is compared with three other initialization algorithms for k-means. And the effectiveness of our approach is shown by a series of carefully designed experiments.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jiangang Qiao
AU  - Yonggang Lu
PY  - 2013/03
DA  - 2013/03
TI  - A new algorithm for choosing initial cluster centers for k-means
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 527
EP  - 530
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.135
DO  - 10.2991/iccsee.2013.135
ID  - Qiao2013/03
ER  -