Automatic Clustering Combining Differential Evolution Algorithm and k-Means Algorithm

  • R. J. Kuo
  • Erma Suryani
  • Achmad Yasid
Conference paper


One of the most challenging problems in data clustering is to determine the number of clusters. This study intends to propose an improved differential evolution algorithm which integrates automatic clustering based differential evolution (ACDE) algorithm and k-means (ACDE-k-means) algorithm. It requires no prior knowledge about number of clusters. k-means algorithm is employed to tune cluster centroids in order to improve the performance of DE algorithm. To validate the performance of the proposed algorithm, two well-known data sets, Iris and Wine, are employed. The computational results indicate that the proposed ACDE-k-means algorithm is superior to classical DE algorithm.


Automatic clustering Differential evolution k-means 


  1. Bandyopadhyay S, Maulik U (2002) Genetic clustering for automatic evolution of cluster and application to image classification. Pattern Recogn 35:1197–1208zbMATHCrossRefGoogle Scholar
  2. Cai D, Xiaofei H, Han J (2005) Documents clustering using locality preserving indexing. Knowl Data Eng IEEE Tran 17(12):1624–1637CrossRefGoogle Scholar
  3. Chou C-H, Su M-C, Lai E (2004) A new cluster validity measure and its application to image compression. Patter Anal Applic 7:205–220MathSciNetGoogle Scholar
  4. Das S, Abraham A, Komar A (2008) Automatic clustering using improved differential evolution algorithm. IEEE Trans Syst Man Cybern Part A 38:218–237CrossRefGoogle Scholar
  5. Frigui H, Krishnapuram R (1999) A robust competitive clustering algorithm with applications in computer vision. IEEE Trans Pattern Anal Mach Intell 21:450–465CrossRefGoogle Scholar
  6. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323CrossRefGoogle Scholar
  7. Kuo RJ, Akbaria K, Subroto B (2012a) Application of particle swarm optimization and perceptual map to tourist market segmentation. Expert Syst App 39:8726–8735CrossRefGoogle Scholar
  8. Kuo RJ, Syu YJ, Chen ZY, Tien FC (2012b) Integration of particle swarm optimization and genetic algorithm for dynamic clustering. Inf Sci 195:124–140CrossRefGoogle Scholar
  9. Kwedlo W (2011) A clustering method combining differential evolution with the k-means algorithms. Pattern Recognit Lett 32:1613–1621CrossRefGoogle Scholar
  10. Macqueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley Symposium on mathematical statistics and probability, vol 1, pp 281–297Google Scholar
  11. Paterlini S, Krink T (2006) Differential evolution and particle swarm optimization in partitional clustering. Comput Stat Data Anal 50:1220–1247MathSciNetCrossRefGoogle Scholar
  12. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359MathSciNetzbMATHCrossRefGoogle Scholar
  13. Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL (2001) Model-based clustering and data transformations for gene expression data. Bioinformatics 17:977–987CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2013

Authors and Affiliations

  1. 1.Department of Industrial ManagementNational Taiwan University of Science and TechnologyTaipeiTaiwan, Republic of China
  2. 2.Department of Information SystemsInstitut Teknologi Sepuluh NopemberSurabayaIndonesia

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