Clustering Analysis using Opposition-based API Algorithm

Mohammad Reza Farmani, Giuliano Armano


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 Harvard Style

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

in Bibtex Style

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,},

in EndNote Style

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