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Clustering of Vehicle Usage Behavior by Means of Artificial Bee Colony

  • Cosimo Birtolo
  • Davide Ronca
  • Giovanni Capasso
  • Gennaro Sorrentino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8467)

Abstract

This paper proposes a new formulation of Artificial Bee Colony (ABC) in order to address clustering problems. The proposed algorithm models the inspector bee within the colony. It is tested for some benchmarks and is adopted to a real-world problem in Transportation System domain. In particular, we propose a clustering problem for the identification of vehicle usage in Poste Italiane by grouping together those vehicles with same features as fuel economies, frequency and value of refueling activities.

Keywords

Artificial Bee Colony Soft Computing Clustering Intelligent Transportation Systems 

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References

  1. 1.
    Talbi, E.G.: Metaheuristics: From Design to Implementation. Wiley Publishing (2009)Google Scholar
  2. 2.
    Karaboga, D., Basturk, B.: On the performance of Artificial Bee Colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)CrossRefGoogle Scholar
  3. 3.
    Abu-Mouti, F., El-Hawary, M.: Overview of Artificial Bee Colony (abc) algorithm and its applications. In: 2012 IEEE International Systems Conference (SysCon), pp. 1–6 (2012)Google Scholar
  4. 4.
    Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31(8), 651–666 (2010)CrossRefGoogle Scholar
  5. 5.
    Fu, X., Wang, L.: Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cyb. 33(3), 399–409 (2003)CrossRefGoogle Scholar
  6. 6.
    Lee, J., Lee, D.: An improved cluster labeling method for support vector clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 461–464 (2005)CrossRefGoogle Scholar
  7. 7.
    Hruschka, E., Campello, R.J.G.B., Freitas, A., De Carvalho, A.C.P.L.F.: A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(2), 133–155 (2009)CrossRefGoogle Scholar
  8. 8.
    Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing 11(1), 652–657 (2011)CrossRefGoogle Scholar
  9. 9.
    Yan, X., Zhu, Y., Zou, W., Wang, L.: A new approach for data clustering using hybrid ABC algorithm. Neurocomput. 97, 241–250 (2012)CrossRefGoogle Scholar
  10. 10.
    Biesmeijer, J.C., de Vries, H.: Exploration and exploitation of food sources by social insect colonies: a revision of the scout-recruit concept. Behavioral Ecology and Sociobiology 49(2-3), 89–99 (2001)CrossRefGoogle Scholar
  11. 11.
    Granovskiy, B., Latty, T., Duncan, M., Sumpter, D.J.T., Beekman, M.: How dancing honey bees keep track of changes: the role of inspector bees. Behavioral Ecology 23(3), 588–596 (2012)CrossRefGoogle Scholar
  12. 12.
    Bache, K., Lichman, M.: UCI machine learning repository (2013)Google Scholar
  13. 13.
    Birtolo, C., Diessa, V., De Chiara, D., Ritrovato, P.: Customer churn detection system: Identifying customers who wish to leave a merchant. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds.) IEA/AIE 2013. LNCS, vol. 7906, pp. 411–420. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Shevade, S., Keerthi, S., Bhattacharyya, C., Murthy, K.: Improvements to SMO algorithm for SVM Regression. Technical report, National University of Singapore, Control Division Dept of Mechanical and Production Engineering, National University of Singapore, Technical Report CD-99-16 (1999)Google Scholar
  15. 15.
    Fader, P.S., Hardie, B.G.S., Lee, K.L.: “Counting your Customers” the easy way: An alternative to the Pareto/NBD Model. Marketing Science 24(2), 275–284 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Cosimo Birtolo
    • 1
  • Davide Ronca
    • 1
  • Giovanni Capasso
    • 1
  • Gennaro Sorrentino
    • 1
  1. 1.Poste Italiane – Information TechnologyNaplesItaly

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