Skip to main content
SpringerLink
Log in

A bio-inspired hierarchical clustering algorithm with backtracking strategy

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Biological entities, such as birds with their flocking behavior, ants with their social colonies, fish with their shoaling behavior and honey bees with their complex nest construction, represent a great source of inspiration in the optimization and data mining domains. Following this line of thought, we propose the Communicating Ants for Clustering with Backtracking strategy (CACB) algorithm, which is based on a dynamic and an adaptive aggregation threshold and a backtracking strategy where artificial ants are allowed to turn back in their previous aggregation decisions. The CACB algorithm is a hierarchical clustering algorithm that generates compact dendrograms since it allows the aggregation of more than two clusters at a time. Its high performance is experimentally shown through several real benchmark data sets and a content-based image retrieval system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Detrain C, Deneubourg JL (2006) Self-organized structures in a superorganism: Do ants “behave” like molecules Phys Life Rev 3(3):162–187

    Article  Google Scholar 

  2. Dorigo M, Gambardella L (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1):53–66

    Article  Google Scholar 

  3. Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy

  4. Bell JE, Mcmullen R (2004) Ant colony optimization techniques for the vehicle routing problem. Adv Eng Inf 18(1):41–48

    Article  Google Scholar 

  5. Parpinelli R, Lopes H, Freitas A (2002) An ant colony algorithm for classification rule discovery. In: Abbass H, Sarker R, Newton C (eds) Data mining: a heurstic approach. Idea Group Publishing, London, pp 191–208

    Chapter  Google Scholar 

  6. Kuo RJ, Shih CW (2007) Association rule mining through the ant colony system for national health insurance research database in taiwan. Comput Math Appl 54(11-12):1303–1318

    Article  MATH  MathSciNet  Google Scholar 

  7. Shelokar P, Jayaraman V, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chimica Acta 509(1):187–195

    Article  Google Scholar 

  8. Lumer E, Faieta B (1994) Diversity and adaptation in populations of clustering ants. In: Proceedings of the third international conference on simulation of adaptive behaviour, pp 501–508

  9. Deneubourg JL, Goss S, Franks N, Sendova-Franks A, Detrain C, Chrétien L (1990) The dynamics of collective sorting robot-like ants and ant-like robots. In: Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats. MIT Press, Cambridge, pp 356–363

    Google Scholar 

  10. Ramos V, Merelo JJ (2002) Self-organized stigmergic document maps: environment as a mechanism for context learning . In: Alba E, Herrera F, Merelo JJ et al (eds) EBA’2002, first spanish conference on evolutionary and bio-inspired algorithms. Spain, pp 284–293

  11. Abraham A, Ramos V (2003) Web usage mining using artificial ant colony clustering and linear genetic programming Genetic programming, congress on evolutionary computation (CEC). IEEE Press, Australia, pp 1384–1391

  12. Azzag H, Venturini G, Oliver A, Guinot C (2007) A hierarchical ant based clustering algorithm and its use in three real-world applications. Eur J Oper Res 179(3):906–922

    Article  MATH  Google Scholar 

  13. Ouadfel S, Batouche M, Garbay C (2002) Ant colony system for image segmentation using markov random field . In: Dorigo M, Caro GD, Sampels M (eds) Ant algorithms, lecture notes in computer science, vol 2463. Springer, pp 294–295

  14. Yang X, Zhao W, Chen Y, Fang X (2008) Image segmentation with a fuzzy clustering algorithm based on ant-tree. Signal Process 88(10):2453–2462

    Article  MATH  Google Scholar 

  15. Han Y, Shi P (2007) An improved ant colony algorithm for fuzzy clustering in image segmentation. Neurocomputing 70(4-6): 665–671

    Article  Google Scholar 

  16. Kuntz P, Snyers D, Layzell PJ (1999) A stochastic heuristic for visualising graph clusters in a bi-dimensional space prior to partitioning. J Heuristics 5(3):327–351

    Article  MATH  Google Scholar 

  17. Gzara M, Jamoussi S, Elkamel A, Ben-Abdallah H (2011) L’algorithme CAC: des fourmis artificielles pour la classification automatique. Revue d’Intelligence Artificielle RSTI série RIA 25(6):767–797

    Article  Google Scholar 

  18. Elkamel A, Jamoussi S, Gzara M, Ben-Abdallah H (2009) An ant-based algorithm for clustering. In: The 7th ACS/IEEE international conference on computer systems and applications, Rabat, pp 76–82

  19. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323

    Article  Google Scholar 

  20. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Cam LML, Neyman J (eds) Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. University of California Press, pp 281–297

  21. Brendan JF, Delbert D (2007) Clustering by passing messages between data points. Science 315:972–977

    Article  MATH  MathSciNet  Google Scholar 

  22. Kansheng S, Leming L (2013) High performance genetic algorithm based text clustering using parts of speech and outlier elimination. Appl Intell 38(4):511–519

    Article  Google Scholar 

  23. Bezdek JC, Boggavarapu S, Hall LO, Bensaid A (1994) Genetic algorithm guided clustering. In: Proceedings of the first IEEE conference on evolutionary computation, ieee world congress on computational intelligence, Orlando, pp 34–39

  24. Naldi MC, de Carvalho ACPLF, Campello RJGB, Hruschka ER (2008) Genetic clustering for data mining. In: Soft computing for knowledge discovery and data mining, pp. 113–132

  25. Masoud H, Jalili S, Hasheminejad SMH (2013) Dynamic clustering using combinatorial particle swarm optimization. Appl Intell 38(3):289–314

    Article  Google Scholar 

  26. Omran M, Salman A, Engelbrecht AP (2002) Image classification using particle swarm optimization. In: Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning 2002 (SEAL 2002). Singapore, pp. 370–374

  27. Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Physica D: Nonlinear Phenomena 22(1–3):187–204

    Article  MathSciNet  Google Scholar 

  28. Monmarché N, Slimane M, Venturini G (2001) L’algorithme antclass: classification non supervisée par une colonie de fourmis artificielles. Extraction des Connaissances et Apprentissage: Apprentissage et évolution 1(3):131–166

    Google Scholar 

  29. Vizine AL, de Castro LN, Hruschka ER, Gudwin RR (2005) Towards improving clustering ants: an adaptive ant clustering algorithm. Informatica (Slovenia) 29(2):143–154

    MATH  Google Scholar 

  30. Ouadfel S, Batouche M (2007) An efficient ant algorithm for swarm based image clustering. J Comput Sci 3(3):162–167

    Article  Google Scholar 

  31. Labroche N, Monmarché N, Venturini G (2002) A new clustering algorithm based on the chemical recognition system of ants . In: Harmelen F (ed) Proceedings of the 15th European conference on artificial intelligence. IOS Press, Lyon, pp 345–349

    Google Scholar 

  32. Azzag H, Monmarché N, Slimane M, Guinot C, Venturini G (2003) AntTree: a new model for clustering with artificial ants. In: Banzhaf W, Christaller T, Dittrich P, Kim JT, Ziegler J (eds) Advances in artificial life - Proceedings of the 7th European conference on artificial life (ECAL), lecture notes in artificial intelligence, vol. 2801. Springer Verlag, Berlin, Heidelberg, pp 564–571

    Google Scholar 

  33. Admane L, Benatchba K, Koudil M, Siad L, Maziz S (2006) Antpart: an algorithm for the unsupervised classification problem using ants. Appl Math Comput 180(1):16–28

    Article  MATH  MathSciNet  Google Scholar 

  34. Handl J, Meyer B (2002) Improved ant-based clustering and sorting in a document retrieval interface. In: Proceedings of the PPSN VII, the 7th international conference on parallel problem solving from nature, lecture notes in computer science, vol 2439. Springer-Verlag, Berlin, pp 913–923

  35. Handl J, Knowles J, Dorigo M (2006) Ant-based clustering and topographic mapping. Artif Life 12(1):35–61

    Article  Google Scholar 

  36. Zhang L, Cao Q (2011) A novel ant-based clustering algorithm using the kernel method. Inf Sci 181(20):4658–4672

    Article  MathSciNet  Google Scholar 

  37. Zhang L, Cao Q, Lee J (2013) A novel ant-based clustering algorithm using renyi entropy. Appl Soft Comput 13(5):2643–2657

    Article  Google Scholar 

  38. Montes de Oca MA, Garrido L, Aguirre JL (2005) Effects of inter-agent communication in ant-based clustering algorithms: a case study on communication policies in swarm systems . In: Gelbukh A, de Albornoz A, Terashima H (eds) Proceedings of the fourth Mexican international conference on artificial intelligence (MICAI 2005), Monterrey, N.L. Mexico, LNAI, vol 3789. Springer, Berlin, pp 254–263

    Google Scholar 

  39. Billen J (2006) Signal variety and communication in social insects. Scanning 17:9–25

    Google Scholar 

  40. Hölldobler B (1999) Multimodal signals in ant communication. J Comp Physiol A: Neuroethology Sens Neural Behav Physiol 184:129–141

    Article  Google Scholar 

  41. Hickling R, Brown RL (2000) Analysis of acoustic communication by ants. J Acoust Soc Am 108 (4):1920–1929

    Article  Google Scholar 

  42. Asuncion A, Newman D (2007) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http://www.ics.uci.edu/~mlearn/MLRepository.html

  43. Lance GN, Williams WT (1967) A general theory of classificatory sorting strategies: hierarchical systems. Comput J 9(4): 373–380

    Article  Google Scholar 

  44. Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, New York

    Book  MATH  Google Scholar 

  45. Manjunath B, Salembier P, Sikora T (2002) Introduction to MPEG-7: multimedia content description interface. Wiley, New York

    Google Scholar 

  46. Spyrou E, Tolias G, Mylonas P, Avrithis Y (2009) Concept detection and keyframe extraction using a visual thesaurus. Multimedia Tools Appl 41(3):337–373

    Article  Google Scholar 

  47. Wang J, Li J, Wiederhold G (2001) Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Multimedia Information systems and Advanced Computing Laboratory (Miracl), Sfax, Tunisia

    Akil Elkamel & Mariem Gzara

  2. Higher School of Computer Sciences and Mathematics, University of Monastir, Monastir, Tunisia

    Akil Elkamel & Mariem Gzara

  3. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Hanêne Ben-Abdallah

Authors
  1. Akil Elkamel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariem Gzara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hanêne Ben-Abdallah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akil Elkamel.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elkamel, A., Gzara, M. & Ben-Abdallah, H. A bio-inspired hierarchical clustering algorithm with backtracking strategy. Appl Intell 42, 174–194 (2015). https://doi.org/10.1007/s10489-014-0573-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-014-0573-6

Keywords

Search

Navigation