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Authors: Mohammad Reza Farmani and Giuliano Armano

Affiliation: University of Cagliari, Italy

ISBN: 978-989-758-157-1

Keyword(s): Pachycondyla Apicalis Ants, Opposition-based, Clustering Analysis.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Swarm/Collective Intelligence ; Symbolic Systems

Abstract: Clustering is a significant data mining task which partitions datasets based on similarities among data. In this study, partitional clustering is considered as an optimization problem and an improved ant-based algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), is applied to automatic grouping of large unlabeled datasets. The proposed algorithm employs Opposition-Based Learning (OBL) for ants' hunting sites generation phase in API. Experimental results are compared with the classical API clustering algorithm and three other recently evolutionary-based clustering techniques. It is shown that the proposed algorithm can achieve the optimal number of clusters and, in most cases, outperforms the other methods on several benchmark datasets in terms of accuracy and convergence speed.

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Paper citation in several formats:
Reza Farmani, M. and Armano, G. (2015). Clustering Analysis using Opposition-based API Algorithm.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 39-47. DOI: 10.5220/0005585700390047

@conference{ecta15,
author={Mohammad Reza Farmani. and Giuliano Armano.},
title={Clustering Analysis using Opposition-based API Algorithm},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={39-47},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005585700390047},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Clustering Analysis using Opposition-based API Algorithm
SN - 978-989-758-157-1
AU - Reza Farmani, M.
AU - Armano, G.
PY - 2015
SP - 39
EP - 47
DO - 10.5220/0005585700390047

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