CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

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
journal cover thumbnail

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.

References
  1. M. S. Kamel and S. Z. Selim, "New algorithms for solving the fuzzy clustering problem," Pattern recognition, vol. 27, pp. 421-428, 1994.
  2. M. Omran, A. Salman, and A. P. Engelbrecht, "Image classification using particle swarm optimization," in Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning, 2002, pp. 18-22.
  3. S. Z. Selim and M. A. Ismail, "K-means-type algorithms: a generalized convergence theorem and characterization of local optimality," IEEE Transactions on pattern analysis and machine intelligence, pp. 81-87, 1984.
  4. M. Fathian, B. Amiri, and A. Maroosi, "Application of honey-bee mating optimization algorithm on clustering," Applied Mathematics and Computation, vol. 190, pp. 1502-1513, 2007.
  5. E. W. Forgy, "Cluster analysis of multivariate data: efficiency versus interpretability of classifications," Biometrics, vol. 21, pp. 768-769, 1965.
  6. C. T. Zahn, "Graph-theoretical methods for detecting and describing gestalt clusters," IEEE Transactions on computers, vol. 100, pp. 68-86, 1971.
  7. T. Mitchell, "Machine Learning, McGraw-Hill Higher Education," New York, 1997.
  8. J. West, J. M. Fitzpatrick, M. Y. Wang, B. M. Dawant, C. R. Maurer Jr, R. M. Kessler, et al., "Comparison and evaluation of retrospective intermodality brain image registration techniques," Journal of computer assisted tomography, vol. 21, pp. 554-568, 1997.
  9. S. Paterlini and T. Minerva, "Evolutionary approaches for cluster analysis," in Soft Computing Applications, ed: Springer, 2003, pp. 165-176.
  10. C.-H. Tsang and S. Kwong, "Ant colony clustering and feature extraction for anomaly intrusion detection," in Swarm Intelligence in Data Mining, ed: Springer, 2006, pp. 101-123.
  11. R. Younsi and W. Wang, "A new artificial immune system algorithm for clustering," in International Conference on Intelligent Data Engineering and Automated Learning, 2004, pp. 58-64.
  12. P. Shelokar, V. K. Jayaraman, and B. D. Kulkarni, "An ant colony approach for clustering," Analytica Chimica Acta, vol. 509, pp. 187-195, 2004.
  13. M. Omran, A. P. Engelbrecht, and A. Salman, "Particle swarm optimization method for image clustering," International Journal of Pattern Recognition and Artificial Intelligence, vol. 19, pp. 297-321, 2005.
  14. S. Z. Selim and K. Alsultan, "A simulated annealing algorithm for the clustering problem," Pattern recognition, vol. 24, pp. 1003-1008, 1991.
  15. K. S. Al-Sultan, "A tabu search approach to the clustering problem," Pattern Recognition, vol. 28, pp. 1443-1451, 1995.
  16. U. Maulik and S. Bandyopadhyay, "Genetic algorithm-based clustering technique," Pattern recognition, vol. 33, pp. 1455-1465, 2000.
  17. Y.-T. Kao, E. Zahara, and I.-W. Kao, "A hybridized approach to data clustering," Expert Systems with Applications, vol. 34, pp. 1754-1762, 2008.
  18. P. Manikandan and S. Selvarajan, "Data clustering using cuckoo search algorithm (CSA)," in Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012, 2014, pp. 1275-1283.
  19. D. Karaboga and B. Akay, "A comparative study of artificial bee colony algorithm," Applied mathematics and computation, vol. 214, pp. 108-132, 2009.
  20. D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Applied soft computing, vol. 8, pp. 687-697, 2008.
  21. L. N. De Castro and F. J. Von Zuben, "Artificial immune systems: Part I–basic theory and applications," Universidade Estadual de Campinas, Dezembro de, Tech. Rep, vol. 210, 1999.
  22. C. Zhang, D. Ouyang, and J. Ning, "An artificial bee colony approach for clustering," Expert Systems with Applications, vol. 37, pp. 4761-4767, 2010.
  23. M. Lichman, "{UCI} Machine Learning Repository," 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial bee colony algorithm Data clustering.

Powered by PhDFocusTM