Ant Colony Optimization and Data Mining

  • Ioannis Michelakos
  • Nikolaos Mallios
  • Elpiniki Papageorgiou
  • Michael Vassilakopoulos
Part of the Studies in Computational Intelligence book series (SCI, volume 352)


The Ant Colony Optimization (ACO) technique was inspired by the ants’ behavior throughout their exploration for food. In nature, ants wander randomly, seeking for food. After succeeding, they return to their nest. During their move, they lay down pheromone that forms an evaporating chemical path. Other ants that locate this trail, follow it and reinforce it, since they also lay down pheromone. As a result, shorter paths to food have more pheromone and are more likely to be followed. ACO algorithms are probabilistic techniques for solving computational problems that are based in finding as good as possible paths through graphs by imitating the ants’ search for food. The use of such techniques has been very successful for several problems. Besides, Data Mining (DM), a discipline that consists of techniques for discovering previously unknown, valid patterns and relationships in large data sets, has emerged as an important technology with numerous practical applications, due to wide availability of a vast amount of data. The collaborative use of ACO and DM (the use of ACO algorithms for DM tasks) is a very promising direction. In this chapter, we review ACO, DM, Classification and Clustering (two of the most popular DM tasks) and focus on the use of ACO for Classification and Clustering. Moreover, we briefly present related applications and examples and outline possible future trends of this promising collaborative use of techniques.


Pheromone Trail Rule Pruning Pheromone Level Pheromone Matrix Total Within Cluster Variance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Angus, D., Woodward, C.: Multiple objective ant colony optimization. Swarm Intelligence 3(1), 69–85 (2009)CrossRefGoogle Scholar
  2. 2.
    Borkar, V.S., Das, D.: A novel ACO algorithm for optimization via reinforcement and initial bias. Swarm Intelligence 3(1), 3–34 (2009)CrossRefGoogle Scholar
  3. 3.
    Boryczka, U.: Finding Groups in Data: Cluster Analysis with Ants. Applied Soft Computing 9(1), 61–70 (2009)CrossRefGoogle Scholar
  4. 4.
    Bursa, M., Lhotska, L.: Ant Colony Cooperative Strategy in Electrocardiogram and Electroencephalogram Data Clustering. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2007), pp. 323–333 (2007)Google Scholar
  5. 5.
    Bursa, M., Lhotska, L., Macas, M.: Hybridized swarm metaheuristics for evolutionary random forest generation. In: 7th International Conference on Hybrid Intelligent Systems (HIS 2007), pp. 150–155 (2007)Google Scholar
  6. 6.
    Chelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An Ant Colony Approach for Clustering. Analytica Chimica Acta 509(2), 187–195 (2004)CrossRefGoogle Scholar
  7. 7.
    Choruengwiwat, P.: Thai handwritten character recognition using extraction of distinctive features. Master’s Thesis, Department of Electrical Engineering, Chulalongkorn University, Thailand (1998)Google Scholar
  8. 8.
    Chudacek, V., Lhotska, L.: Unsupervised creation of heart beats classes from long-term ECG monitoring. In: 18th International Conference of European Association for Signal Processing (EURASIP) Biosignals, pp. 199–201 (2006)Google Scholar
  9. 9.
    Corry, P., Kozan, E.: Ant colony optimisation for machine layout problems. Computational Optimization and Applications 28(3), 287–310 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The Dynamics of Collective Sorting: Robot-like Ants and Ant-like Robots. In: From Animals to Animats, 1st International Conference on Simulation of Adaptive Behaviour, pp. 356–363 (1990)Google Scholar
  11. 11.
    Dorigo, M.: Optimization, Learning and Natural Algorithms. PhD thesis, Politecnico di Milano, Italie (1992)Google Scholar
  12. 12.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  13. 13.
    Dorigo, M., Gambardelaa, L.M.: Ant Colony System:A cooperative Learning Approach to Travelling Salesman Problem. IEEE Trans. Evol. Comp. 1, 53–66 (1997)CrossRefGoogle Scholar
  14. 14.
    Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P., Mark, R., Mietus, J., Moody, G., Peng, C., Stanley, H.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23), 215–220 (2000)Google Scholar
  15. 15.
    Grasse, P.: La reconstruction du nid et les coordinations inter-individuelles chez bellicositermes natalensis et cubitermes sp. La theorie de la stigmergie: Essai d’interperation des termites constructeurs. Insectes Sociaux 6, 41–81 (1959)CrossRefGoogle Scholar
  16. 16.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2006)Google Scholar
  17. 17.
    Hu, X., Zhang, J., Li, Y.: Orthogonal methods based ant colony search for solving continuous optimization problems. Journal of Computer Science and Technology 23(1), 2–18 (2008)CrossRefGoogle Scholar
  18. 18.
    Jiang, W., Xu, Y., Xu, Y.: A novel data mining method based on ant colony algorithm. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 284–291. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Jin, P., Zhu, Y., Hu, K., Li, S.: Classification Rule Mining Based on Ant Colony Optimization Algorithm. In: International Conference on Intelligent Computing (ICIC 2006). LNCIST, vol. 344, pp. 654–663. Springer, Heidelberg (2006)Google Scholar
  20. 20.
    Kantardzic, M., Zurada, J. (eds.): Next Generation of Data-Mining Applications. Wiley-IEEE Press, Chichester (2005)zbMATHGoogle Scholar
  21. 21.
    Kargupta, H., et al.: Collective Data Mining. In: Karhgupta, Chan (eds.) Advances in Distributed Data Mining. MIT Press, Cambridge (2000)Google Scholar
  22. 22.
    Masaomi, K.: Application of Data Mining Techniques to the Data Analyses to Ensure Safety of Medicine Usage. In: Ponce, J., Karahoca, A. (eds.) Data Mining and Knowledge Discovery in Real Life Applications. I-Tech Education and Publishing (2009)Google Scholar
  23. 23.
    Kumar, S., Rao, C.: Application of ant colony, genetic algorithm and data mining-based techniques for scheduling. Robotics and Computer-Integrated Manufacturing 25, 901–908 (2009)CrossRefGoogle Scholar
  24. 24.
    Kuo, R.J., Lin, S.Y., Shih, C.W.: Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan. Expert Systems with Applications 33, 794–808 (2007)CrossRefGoogle Scholar
  25. 25.
    Kuo, R.J., Wang, H.S., Hu, T.L., Chou, S.H.: Application of ant K-means on clustering analysis. Computers & Mathematics with Applications 50, 1709–1724 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  26. 26.
    Lioni, A., Sauwens, C., Theraulaz, G., Deneubourg, J.L.: Chain formation in Oecophylla longinoda. Journal of Insect Behavior 14, 679–696 (2001)CrossRefGoogle Scholar
  27. 27.
    Liu, B., Abbass, H.A., McKay, B.: Classification rule discovery with ant colony optimization. IEEE Computational Intelligence Bulletin 3(1), 31–35 (2004)Google Scholar
  28. 28.
    Liu, H., Hussain, F., Tan, C.L., Dash, M.: Discretization: An enabling technique. Data Mining and Knowledge Discovery 6, 393–423 (2002)CrossRefMathSciNetGoogle Scholar
  29. 29.
    Lopez-Ibanez, M., Blum, C.: Beam-ACO for the traveling salesman problem with time windows. Computers & Operations Research 37(9), 1570–1583 (2010)zbMATHCrossRefMathSciNetGoogle Scholar
  30. 30.
    Lumer, E.D., Faieta, B.: Diversity and Adaptation in Populations of Clustering Ants, From Animals to Animats. In: 3rd International Conference on the Simulation of Adaptive Behaviour, pp. 501–508 (1994)Google Scholar
  31. 31.
    Mhamdi, F., Elloumi, M.: A new survey on knowledge discovery and data mining. In: 2nd IEEE Int. Conf. on Research Challenges in Information Science, pp. 427–432 (2008)Google Scholar
  32. 32.
    Michelakos, I., Papageorgiou, E., Vasilakopoulos, M.: A Hybrid Classification Algorithm evaluated on Medical Data. In: 1st International Workshop on Cooperative Knowledge Discovery & Data Mining / 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises (CKDD / WETICE), pp. 98–103 (2010)Google Scholar
  33. 33.
    Michelakos, I., Papageorgiou, E., Vasilakopoulos, M.: A Study of cAnt-Miner2 Parameters Using Medical Data Sets. In: 1st International Workshop on Cooperative Knowledge Discovery & Data Mining / 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises (CKDD / WETICE), pp. 119–121 (2010)Google Scholar
  34. 34.
    Monmarche, N., Slimane, M., Venturini, G.: On improving clustering in numerical database with artificial ants. In: Floreano, D., Mondada, F. (eds.) ECAL 1999. LNCS (LNAI), vol. 1674, pp. 626–635. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  35. 35.
    Moss, J.D., Johnson, C.G.: An ant colony algorithm for multiple sequence alignment in bioinformatics. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds.) Artificial Neural Networks and Genetic Algorithms, pp. 182–186. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  36. 36.
    Mullen, R.J., Monekosso, D., Barman, S., Remagnino, P.: A review of ant algorithms. Expert Systems with Applications 36, 9608–9617 (2009)CrossRefGoogle Scholar
  37. 37.
    Neumann, F., Sudholt, D., Witt, C.: Comparing variants of MMAS ACO algorithms on pseudo-boolean functions. In: Stützle, T., Birattari, M., Hoos, H.H. (eds.) SLS 2007. LNCS, vol. 4638, pp. 61–75. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  38. 38.
    Otero, F.E.B., Freitas, A.A., Johnson, C.G.: A Hierarchical Classification Ant Colony Algorithm for Predicting Gene Ontology Terms. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2009. LNCS, vol. 5483, pp. 339–357. Springer, Heidelberg (2009)Google Scholar
  39. 39.
    Otero, F.E.B., Freitas, A.A., Johnson, C.G.: cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 48–59. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  40. 40.
    Otero, F.E.B., Freitas, A.A., Johnson, C.G.: Handling continuous attributes in ant colony classification algorithms. In: IEEE Symposium on Computational Intelligence in Data Mining (CIDM), pp. 225–231 (2009)Google Scholar
  41. 41.
    Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: An ant colony based system for data mining: applications to medical data. In: Genetic and Evolutionary Computation Conference (GECC0 2001), pp. 791–797 (2001)Google Scholar
  42. 42.
    Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6, 321–332 (2002)CrossRefGoogle Scholar
  43. 43.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1226–1238 (2005)CrossRefGoogle Scholar
  44. 44.
    Pérez-Delgado, M.: Rank-Based Ant System to Solve the Undirected Rural Postman Problem. In: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, pp. 507–514 (2009)Google Scholar
  45. 45.
    Phokharatkul, P., Sankhuangaw, K., Somkuarnpanit, S., Phaiboon, S., Kimpan, C.: Off-Line Hand Written Thai Character Recognition using Ant-Miner Algorithm. Transactions on ENFORMATIKA on Systems Sciences and Engineering 8, 276–281 (2005)Google Scholar
  46. 46.
    Prather, J.C., Lobach, D.F., Goodwin, L.K., Hales, J.W., Hage, M.L., Hammond, W.E.: Medical Data Mining: Knowledge Discovery in a Clinical Data Warehouse. In: Annual Conference of the American Medical Informatics Association, pp. 101–105 (1997)Google Scholar
  47. 47.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  48. 48.
    Shmygelska, A., Aguirre-Hernández, R., Hoos, H.H.: An ant colony optimization algorithm for the 2D HP protein folding problem. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) ANTS 2002. LNCS, vol. 2463, pp. 40–52. Springer, Heidelberg (2002)Google Scholar
  49. 49.
    Shmygelska, A., Hoos, H.H.: An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BioMed Central Bioinformatics 6(30) (2005)Google Scholar
  50. 50.
    Solnon, C.: Combining two pheromone structures for solving the car sequencing problem with Ant Colony Optimization. European Journal of Operational Research 191(3), 1043–1055 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  51. 51.
    Stützle, T., Hoos, H.H.: MAX MIN Ant System. Future Generation Computer Systems 16, 889–914 (2000)CrossRefGoogle Scholar
  52. 52.
    Taniar, D. (ed.): Research and Trends in Data Mining Technologies and Applications. Idea Group Publishing, USA (2007)Google Scholar
  53. 53.
    Thangavel, K., Jaganathan, P.: Rule Mining Algorithm with a New Ant Colony Optimization Algorithm. In: International Conference on Computational Intelligence and Multimedia Applications, pp. 135–140 (2007)Google Scholar
  54. 54.
    Theraulaz, G., Bonabeau, E., Sauwens, C., Deneubourg, J.L., Lioni, A., Libert, F., Passera, L., Solé, R.: Model of droplet dynamics in the Argentine ant Linepithema humile (Mayr). Bulletin of Mathematical Biology 63, 1079–1093 (2001)CrossRefGoogle Scholar
  55. 55.
    Tiwari, R., Husain, M., Gupta, S., Srivastava, A.: Improving ant colony optimization algorithm for data clustering. In: International Conference and Workshop on Emerging Trends in Technology, pp. 529–534 (2010)Google Scholar
  56. 56.
    Tsai, C.F., Tsai, C.W., Wu, H.C., Yang, T.: ACODF: a novel data clustering approach for data mining in large databases. Journal of Systems and Software 73(1), 133–145 (2004)CrossRefGoogle Scholar
  57. 57.
    Wang, Z., Feng, B.: Classification rule mining with an improved ant colony algorithm. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 357–367. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  58. 58.
    White, T., Kaegi, S., Oda, T.: Revisiting elitism in ant colony optimization. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 122–133. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  59. 59.
    Yang, J., Shi, X., Marchese, M., Liang, Y.: An ant colony optimization method for generalized TSP problem. Progress in Natural Science 18(11), 1417–1422 (2008)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ioannis Michelakos
    • 1
  • Nikolaos Mallios
    • 2
  • Elpiniki Papageorgiou
    • 2
  • Michael Vassilakopoulos
    • 1
  1. 1.Dept. of Computer Science & Biomedical InformaticsUniversity of Central GreeceLamiaGreece
  2. 2.Dept. of Informatics and Computer TechnologyTechnological Educational Institute of LamiaLamiaGreece

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