loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: François LaPlante 1 ; Nabil Belacel 2 and Mustapha Kardouchi 1

Affiliations: 1 Université de Moncton, Canada ; 2 National Research Council - Information and Communications Technologies, Canada

Keyword(s): Data-mining, Automatic Clustering, Unsupervised Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

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 cl uster 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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.222.182.249

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 201-210. DOI: 10.5220/0004925902010210

@conference{icaart14,
author={Fran\c{C}ois 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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - LaPlante, F.
AU - Belacel, N.
AU - Kardouchi, M.
PY - 2014
SP - 201
EP - 210
DO - 10.5220/0004925902010210
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic