Research Article
Introducing Hybrid model for Data Clustering using K-Harmonic Means and Gravitational Search Algorithms
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 88 - Issue 17 |
| Published: February 2014 |
| Authors: Anuradha D. Thakare, Rohini S Hanchate |
10.5120/15445-4002
|
Anuradha D. Thakare, Rohini S Hanchate . Introducing Hybrid model for Data Clustering using K-Harmonic Means and Gravitational Search Algorithms. International Journal of Computer Applications. 88, 17 (February 2014), 17-23. DOI=10.5120/15445-4002
@article{ 10.5120/15445-4002,
author = { Anuradha D. Thakare,Rohini S Hanchate },
title = { Introducing Hybrid model for Data Clustering using K-Harmonic Means and Gravitational Search Algorithms },
journal = { International Journal of Computer Applications },
year = { 2014 },
volume = { 88 },
number = { 17 },
pages = { 17-23 },
doi = { 10.5120/15445-4002 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Anuradha D. Thakare
%A Rohini S Hanchate
%T Introducing Hybrid model for Data Clustering using K-Harmonic Means and Gravitational Search Algorithms%T
%J International Journal of Computer Applications
%V 88
%N 17
%P 17-23
%R 10.5120/15445-4002
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Clustering is a process of extracting reliable, unique, effective and comprehensible patterns from database. Various clustering methods are proposed to accomplish exactness and accuracy of clusters. K-Means is well known clustering algorithm but it easily converge to local optima. To overcome this drawback, an improved algorithm called K-Harmonic Mean (KHM) was proposed, which is independent of cluster center initialization. This article presents study of hybridization KHM with other clustering algorithms. In order to improve the clustering accuracy the authors proposed new hybrid KHM model.
References
- International journal of emerging technology and advanced engineering 'comparison of various clustering algorithm of weka tools' may 2012.
- Bangorn klabbankoh, ouen pinngern ph. d. An Applied Genetic Algorithms in Information Retrieval, king mongkut's institute of technology ladkrabang ladkrabang bangkok 10520.
- International journal of emerging technology and advanced engineering "comparison of various clustering algorithm of weka tools" may 2012.
- Bin Zhang, Meichun Hsu, K-Harmonic Means - A Data Clustering Algorithm Umeshwar Dayal Software Technology Laboratory HP Laboratories Palo Alto HPL-1999-124 October, 1999.
- Hua Jiang *, Shenghe Yi, Jing Li, Fengqin Yang, Xin Hu "Ant clustering algorithm with K-harmonic means clustering", 2010.
- Cheng Huang Hung, Hua-Min Chiou ,Wei-Ning Yang "Candidate groups search for K-harmonic means data clustering", 2013.
- Cui, X. , and Potok, T. E. (2005), Document clustering using particle swarm optimization. In: IEEE swarm intelligence symposium. Pasadena, California. Dalli, A, Adaptation of the F-measure to cluster-based Lexicon quality evaluation, In EACL 2003, Budapest.
- Fengqin Yang a,b,*, Tieli Sun a, Changhai Zhang "An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization",2009.
- Abdulrahman Alguwaizani, Pierre Hansen, Nenad Mladenovic, Eric Ngai "Variable neighborhood search for harmonic means clustering", 2011.
- Habiba Drias, Ilyes Khennak, Anis B, "A Hybrid Genetic Algorithm for large scale Information Retrieval", 978-1-4244-4738-1/09/ ©2009 IEEE.
Index Terms
Keywords