K-Means Clustering Efficient Algorithm with Initial Class Center Selection

DOI

10.2991/iwmecs-18.2018.65How to use a DOI?

Keywords

Clustering algorithm; particle swarm optimization algorithm; dissimilarity matrix; k-means

Abstract

The algorithm herein adopts density-based method and max-min distance method to define initial clustering center to eliminate the need for defining clustering center in advance in k-means algorithm, and normalize the data set to reduce the influence of fluctuation of attribute value for each dimension of sample set on accuracy of clustering result. Besides, it obtains dissimilarity matrix and takes advantage of good global convergence ability of particle swarm optimization algorithm to improve proneness of K-means algorithm to be trapped in local optimum. The effectiveness of the algorithm was verified via experiment. However, although the algorithm herein performs well in part of small low dimensional data set, while how to effectively make cluster analysis on large high dimensional data still needs to be further researched.

Copyright

© 2018, 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/).

Download article (PDF)

TY  - CONF
AU  - Suyu Huang
AU  - Pingfang Hu
PY  - 2018/04
DA  - 2018/04
TI  - K-Means Clustering Efficient Algorithm with Initial Class Center Selection
BT  - Proceedings of the 2018 3rd International Workshop on Materials Engineering and Computer Sciences (IWMECS 2018)
PB  - Atlantis Press
SP  - 301
EP  - 305
SN  - 2352-538X
UR  - https://doi.org/10.2991/iwmecs-18.2018.65
DO  - 10.2991/iwmecs-18.2018.65
ID  - Huang2018/04
ER  -