CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Hybrid Particle Swarm Optimization (HPSO) for Data Clustering

by Sandeep U. Mane, Pankaj G. Gaikwad
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 97 - Number 19
Year of Publication: 2014
Authors: Sandeep U. Mane, Pankaj G. Gaikwad
10.5120/17112-7514

Sandeep U. Mane, Pankaj G. Gaikwad . Hybrid Particle Swarm Optimization (HPSO) for Data Clustering. International Journal of Computer Applications. 97, 19 ( July 2014), 1-5. DOI=10.5120/17112-7514

@article{ 10.5120/17112-7514,
author = { Sandeep U. Mane, Pankaj G. Gaikwad },
title = { Hybrid Particle Swarm Optimization (HPSO) for Data Clustering },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 97 },
number = { 19 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume97/number19/17112-7514/ },
doi = { 10.5120/17112-7514 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:25:01.324691+05:30
%A Sandeep U. Mane
%A Pankaj G. Gaikwad
%T Hybrid Particle Swarm Optimization (HPSO) for Data Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 97
%N 19
%P 1-5
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is the collection of different techniques. Clustering information into various cluster is one of the data mining technique. It is a method, in which each cluster must contain more similar data and have much dissimilarity between inter cluster data. Most of traditional clustering algorithms have disadvantages like initial centroid selection, local optima, low convergence rate etc. Clustering with swarm based algorithms is emerging as an alternative to more conventional clustering techniques. In this paper, a new hybrid sequential clustering approach is proposed, which uses PSO - a swarm based technique in sequence with Fuzzy k - means algorithm in data clustering. Experimentation was performed on standard dataset available online. From the result, the proposed approach helps to overcome limitations of both algorithms, improves quality of formed cluster and avoids being trapped in local optima.

References
  1. Ahmadyfard, Alireza, and Hamidreza Modares. Combining pso and k-means to enhance data clustering. In IEEE International symposium on telecommunications, pages 688–692, 2008.
  2. A. Carlisle and G. Dozier. An off-the shelf pso. In Workshop on Particle Swarm Optimization, Indianapolis, IN, pages 1–6, 2001.
  3. Vladimir Estivill Castro. Why so many clustering algorithms a position paper. SIGKDD Exploration, 4(1):66–75, January 2002.
  4. Doring Christian, Marie-Jeanne Lesot, and Rudolf Kruse. Data analysis with fuzzy clustering methods. Computational Statistics Data Analysis, 51(1):192–214, 2006.
  5. Kao IW Tsai CY and Wang YC. An effective particle swarm optimization method for data clustering. In IEEE International Conference Industrial Engineering and Engineering Management, pages 548–552, 2–4 December 2007.
  6. Shi Y. H. and Eberhart R. C. Parameter Selection in Particle Swarm Optimization, volume VII of Evolutionary Programming. Springer Berlin Heidelberg, 1998.
  7. Kennedy J. and Eberhart R. C. Particle swarm optimization. In IEEE International Conference on Neural Networks, Perth Australia, volume 4, pages 1942–1948, 1995.
  8. Hamed Khoshdel and Barat Saman. A new hybrid learningbased algorithm for data clustering. In The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), 2012.
  9. Esmin A Pereira D L and de Araujo F. Study of different approach to clustering data by using particle swarm optimization algorithm. In IEEE World Congress on Computational Intelligence Evolutionary Computation, CEC 2008, volume 1, pages 1817–1822, 1–6 June 2008.
  10. Li Lifen and Zhang Changming. Alert clustering using integrated som/pso. In International Conference on Computer Design and Applications (ICCDA), volume 2, pages 517–574, 2010.
  11. Van Der Merwe and A. P Engelbrecht. Data clustering using particle swarm optimization. In The 2003 Congress on Evolutionary Computation, 2003. CEC 03, volume 1, pages 215– 220, 8–12 December 2003.
  12. Taher Niknam and Babak Amiri. An efficient hybrid approach based on pso, aco and k-means for cluster analysis. Applied Soft Computing, 10. 1:183–197, 2010.
  13. Jain A. R. , Murthy M. N. , and Flynn P. J. Data clustering: A review. ACM Computing Surveys, 31(3):265 –323, 1999.
  14. Sandeep Rana, Sanjay Jasola, and Rajesh Kumar. A hybrid sequential approach for data clustering using k-means and particle swarm optimization algorithm. International Journal of Engineering, Science and Technology, MultiCraft Limited, 2(6):167–176, 2010.
  15. Vinod Sharma, Nitish Salwan, Sandeep Singh, Navneet Singh Babra, and Prabhsimran Singh. A review of data clustering techniques and enhancement of data clustering using hybrid clustering model of k-means and pso clustering. International Journal on Advanced Computer Theory and Engineering (IJACTE),, 2(2):2319 – 2526, 2013.
  16. He Y. Pan W. and Lin J. Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data. Computer Stat Data Anal 51, pages 641–658, 2006.
  17. Kao Y-T, Zahara E, and Kao I-W. A hybridized approach to data clustering. Expert Syst with Appl, 34(3):1754–1762, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Data Clustering Particle Swarm Optimization Fuzzy k-means Hybrid Particle Swarm Optimization

Powered by PhDFocusTM