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Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

by Zeeshan Danish, Ahmed Hassan, Akhtar Badshah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 11
Year of Publication: 2018
Authors: Zeeshan Danish, Ahmed Hassan, Akhtar Badshah
10.5120/ijca2018916218

Zeeshan Danish, Ahmed Hassan, Akhtar Badshah . Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering. International Journal of Computer Applications. 180, 11 ( Jan 2018), 1-5. DOI=10.5120/ijca2018916218

@article{ 10.5120/ijca2018916218,
author = { Zeeshan Danish, Ahmed Hassan, Akhtar Badshah },
title = { Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 180 },
number = { 11 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number11/28903-2018916218/ },
doi = { 10.5120/ijca2018916218 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:00:21.144652+05:30
%A Zeeshan Danish
%A Ahmed Hassan
%A Akhtar Badshah
%T Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 11
%P 1-5
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data clustering is a widespread data compression, vector quantization, data analysis and data mining technique. The principle objective of data clustering is to make clusters (or groups) such that data having high degree of similarity is gathered in the same cluster while data having high degree of dissimilarity is gathered in the different clusters and plays a key role for users to organize, summarize, and steer the data adequately. In this work Artificial Bee Colony (ABC) algorithm is applied to different size datasets. Results clearly show that ABC when applied on small size datasets were more effective than those of large size datasets in terms of intra- cluster distance, computation cycles and time required to complete those cycles.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial bee colony algorithm Data clustering.

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