Performance analysis of firefly algorithm for data clustering

Extraction of relevant information from web is gaining immense significance. This depends upon the efficacy of methods employed to represent, organise and access the information. The most important technique that is used to organise the data is clustering. Clustering problem refers to partitioning unlabeled data objects into certain number of clusters with the objective of maximum homogeneity within cluster and heterogeneity between the clusters. The paper studies viability of firefly algorithm for clustering. It incorporates the flashing behaviour of fireflies to achieve the optimal solution. The performance of the proposed algorithm FClust is compared with particle swarm optimisation (PSO) and differential evolution (DE) algorithms with respect to varied statistical criteria using artificial and benchmark datasets. A detailed convergence behaviour of algorithms is studied using run length distribution. The experimental results prove that the proposed algorithm performs better in terms of speed and success rate as compared to PSO and DE.

[1]  Slawomir Zak,et al.  Firefly Algorithm for Continuous Constrained Optimization Tasks , 2009, ICCCI.

[2]  V. Mani,et al.  Clustering using firefly algorithm: Performance study , 2011, Swarm Evol. Comput..

[3]  K. Rameshkumar,et al.  Comparative evaluation of Particle Swarm Optimization Algorithms for Data Clustering using real world data sets , 2008 .

[4]  Sandra Paterlini,et al.  Differential evolution and particle swarm optimisation in partitional clustering , 2006, Comput. Stat. Data Anal..

[5]  Thatchai Thepphakorn,et al.  Application of Firefly Algorithm and Its Parameter Setting for Job Shop Scheduling , 2012 .

[6]  Olvi L. Mangasarian,et al.  Nuclear feature extraction for breast tumor diagnosis , 1993, Electronic Imaging.

[7]  Suyanto,et al.  Evolutionary Discrete Firefly Algorithm for Travelling Salesman Problem , 2011, ICAIS.

[8]  Hema Banati,et al.  Fire Fly Based Feature Selection Approach , 2011 .

[9]  Ujjwal Maulik,et al.  Genetic clustering for automatic evolution of clusters and application to image classification , 2002, Pattern Recognit..

[10]  Sanghamitra Bandyopadhyay,et al.  Theoretical performance of genetic pattern classifier , 1999 .

[11]  Sanghamitra Bandyopadhyay,et al.  Pattern classification with genetic algorithms , 1995, Pattern Recognit. Lett..

[12]  U. Hönig,et al.  A Firefly Algorithm-based Approach for Scheduling Task Graphs in Homogeneous Systems , 2010 .

[13]  B. Kulkarni,et al.  An ant colony approach for clustering , 2004 .

[14]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[15]  D. Coomans,et al.  The application of linear discriminant analysis in the diagnosis of thyroid diseases , 1978 .

[16]  W. Welch.  Algorithmic complexity: three NP- hard problems in computational statistics , 1982 .

[17]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[18]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[19]  Ujjwal Maulik,et al.  An evolutionary technique based on K-Means algorithm for optimal clustering in RN , 2002, Inf. Sci..

[20]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[21]  Shokri Z. Selim,et al.  A simulated annealing algorithm for the clustering problem , 1991, Pattern Recognit..

[22]  Sanghamitra Bandyopadhyay,et al.  Simulated Annealing Based Pattern Classification , 1998, Inf. Sci..