By Topic

A new hybrid approach for data clustering using firefly algorithm and K-means

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Hassanzadeh, T. ; Dept. of Comput. Eng. & IT, Azad Univ., Qazvin, Iran ; Meybodi, M.R.

Data clustering is a common technique for data analysis and is used in many fields, including data mining, pattern recognition and image analysis. K-means clustering is a common and simple approach for data clustering but this method has some limitation such as local optimal convergence and initial point sensibility. Firefly algorithm is a swarm based algorithm that use for solving optimization problems. This paper presents a new approach to using firefly algorithm to cluster data. It is shown how firefly algorithm can be used to find the centroid of the user specified number of clusters. The algorithm then extended to use k-means clustering to refined centroids and clusters. This new hybrid algorithm called K-FA. The experimental results showed the accuracy and capability of proposed algorithm to data clustering.

Published in:

Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on

Date of Conference:

2-3 May 2012