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

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...

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Bibliographic Details
Published in2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing pp. 007 - 011
Main Authors Hassanzadeh, Tahereh, Meybodi, Mohammad Reza
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2012
Subjects
Algorithm design and analysis
clustering
Clustering algorithms
Equations
firefly algorithm
k-means
Mathematical model
Optimization
Signal processing algorithms
Vectors
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ISBN9781467314787
1467314781
DOI10.1109/AISP.2012.6313708

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Summary: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.
ISBN:9781467314787
1467314781
DOI:10.1109/AISP.2012.6313708