A Hierarchical Clustering Based Heuristic for Automatic Clustering

François LaPlante, Nabil Belacel, Mustapha Kardouchi

2014

Abstract

Determining an optimal number of clusters and producing reliable results are two challenging and critical tasks in cluster analysis. We propose a clustering method which produces valid results while automatically determining an optimal number of clusters. Our method achieves these results without user input pertaining directly to a number of clusters. The method consists of two main components: splitting and merging. In the splitting phase, a divisive hierarchical clustering method (based on the DIANA algorithm) is executed and interrupted by a heuristic function once the partial result is considered to be “adequate”. This partial result, which is likely to have too many clusters, is then fed into the merging method which merges clusters until the final optimal result is reached. Our method’s effectiveness in clustering various data sets is demonstrated, including its ability to produce valid results on data sets presenting nested or interlocking shapes. The method is compared with cluster validity analysis to other methods to which a known optimal number of clusters is provided and to other automatic clustering methods. Depending on the particularities of the data set used, our method has produced results which are roughly equivalent or better than those of the compared methods.

Paper Citation

in Harvard Style

LaPlante F., Belacel N. and Kardouchi M. (2014). A Hierarchical Clustering Based Heuristic for Automatic Clustering . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 201-210. DOI: 10.5220/0004925902010210

in Bibtex Style

@conference{icaart14,
author={François LaPlante and Nabil Belacel and Mustapha Kardouchi},
title={A Hierarchical Clustering Based Heuristic for Automatic Clustering},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={201-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004925902010210},
isbn={978-989-758-015-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A Hierarchical Clustering Based Heuristic for Automatic Clustering
SN - 978-989-758-015-4
AU - LaPlante F.
AU - Belacel N.
AU - Kardouchi M.
PY - 2014
SP - 201
EP - 210
DO - 10.5220/0004925902010210