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Introducing Interactive Evolutionary Computation in Data Clustering

  • Anna Russo
  • Onofrio GigliottaEmail author
  • Francesco Palumbo
  • Orazio Miglino
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 445)

Abstract

Data clustering consists in finding homogeneous groups in a dataset. The importance attributed to cluster analysis is related to its fundamental role in many knowledge fields. Often data clustering techniques are the ghost host of many innovative applications for a wide range of problems (i.e. biology, marketing, customers segmentation, intelligent machines, machine translation, etc.). Recently, there is an emerging interest in Data Clustering community to develop bio-inspired algorithms in order to find new methods for clustering. It is widely observed that bio-inspired algorithms and the Evolutionary Computation (EC) techniques reach solutions similar to others computational approaches but using a bigger computational power. This limitation represents a concrete obstacle to an extensive use of Evolutionary (or bio-inspired) approach to data clustering applications. In the present paper we propose to use Interactive Evolutionary Computation (IEC) techniques where a human being (the breeder) selects Cluster configurations (genotypes) on the basis of their graphical visualizations (phenotypes). We describe a first version of a software, called Revok, that implements the IEC basic principles applied to data clustering. In the conclusion section we outline the necessary steps to reach a mature IEC tool for data clustering.

Keywords

Interactive Evolutionary Computation Data mining 

References

  1. 1.
    Abul Hasan, M.J., Ramakrishnan, S.: A survey: hybrid evolutionary algorithms for cluster analysis. Artif. Intell. Rev. 36(3), 179–204 (2011)CrossRefGoogle Scholar
  2. 2.
    Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recogn. 35(6), 1197–1208 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Ben-Israel, A., Iyigun, C.: Probabilistic d-clustering. J. Classif. 25(1), 5–26 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Brintrup, A., Ramsden, J., Tiwari, A.: A review on design optimisation and exploration with interactive evolutionary computation. In: Tiwari, A., Roy, R., Knowles, J., Avineri, E., Dahal, K. (eds.) Applications of Soft Computing. AISC, vol. 36, pp. 111–120. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Du, K.L.: Clustering: a neural network approach. Neural Netw. 23(1), 89–107 (2010)CrossRefGoogle Scholar
  6. 6.
    Everitt, B., Landau, S., Leese, M.: Cluster Analysis. A Hodder Arnold Publication. Wiley, New York (2001)Google Scholar
  7. 7.
    Goodwin, C.J.: A History of Modern Psychology. Wiley, New York (2002)Google Scholar
  8. 8.
    Hruschka, E.R., Campello, R.J.G.B., Freitas, A.A., De Carvalho, A.C.P.L.F.: A survey of evolutionary algorithms for clustering. Trans. Syst. Man Cybern. Part C 39(2), 133–155 (2009)CrossRefGoogle Scholar
  9. 9.
    Kihlstrom, J.: The cognitive unconscious. Science 237(4821), 1445–1452 (1987)CrossRefGoogle Scholar
  10. 10.
    Lee, J.Y., Cho, S.B.: Sparse fitness evaluation for reducing user burden in interactive genetic algorithm. In: 1999 IEEE International Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE ’99, vol. 2, pp. 998–1003 (1999)Google Scholar
  11. 11.
    Lumley, T.: R Fundamentals and Programming Techniques. Chapman & Hall/CRC, Boca Raton (2006)Google Scholar
  12. 12.
    Menardi, G.: Density-based silhouette diagnostics for clustering methods. Stat. Comput. 21(3), 295–308 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Miglino, O., Gigliotta, O., Ponticorvo, M., Stefano, N.: Breedbot: an evolutionary robotics application in digital content. Electron. Libr. 26(3), 363–373 (2008)CrossRefGoogle Scholar
  14. 14.
    Parmee, I., Bonham, C.: Cluster-oriented genetic algorithms to support interactive designer/evolutionary computing systems. In: Proceedings of the 1999 Congress on Evolutionary Computation, 1999. CEC 99, vol. 1, pp. 546–553 (1999)Google Scholar
  15. 15.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008). http://www.R-project.org, ISBN 3-900051-07-0
  16. 16.
    Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)CrossRefzbMATHGoogle Scholar
  17. 17.
    Sheikh, R., Raghuwanshi, M.M., Jaiswal, A.: Genetic algorithm based clustering: a survey. In: First International Conference on Emerging Trends in Engineering and Technology, 2008. ICETET ’08, pp. 314–319 (2008)Google Scholar
  18. 18.
    Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 89(9), 1275–1296 (2001)CrossRefGoogle Scholar
  19. 19.
    Teh, C.S., Chen, C.J.: Interactive evolutionary computation and density-based clustering for data analysis. In: International Conference on Intelligent and Advanced Systems, 2007. ICIAS 2007, pp. 104–108 (2007)Google Scholar
  20. 20.
    Xu, R., Wunsch II, D.: Survey of clustering algorithms. Trans. Neural Netw. 16(3), 645–678 (2005)CrossRefGoogle Scholar
  21. 21.
    Yang, Y., Kamel, M.S.: An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recogn. 39(7), 1278–1289 (2006)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anna Russo
    • 1
  • Onofrio Gigliotta
    • 1
    Email author
  • Francesco Palumbo
    • 1
  • Orazio Miglino
    • 1
    • 2
  1. 1.Natural and Artificial Cognition LaboratoryUniversity of Naples Federico IINaplesItaly
  2. 2.Institute of Cognitive Sciences and TechnologiesCNRRomeItaly

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