Cluster Computing

, Volume 21, Issue 1, pp 453–467 | Cite as

EPACO: a novel ant colony optimization for emerging patterns based classification

  • Zulfiqar AliEmail author
  • Waseem Shahzad


In this paper, a novel approach for discovering emerging patterns has been proposed. Majority of the existing algorithms for the discovery of emerging patterns are tree-based which involve growth and shrinking of trees for this purpose. These algorithms follow greedy search approach for discovery of emerging patterns. The proposed approach utilizes the diversity of ant colony optimization and avoids complexity and greedy search of tree-based algorithms for discovery of emerging patterns. The experiments show that the proposed approach provides higher accuracy than existing state of the art classifiers as well as emerging pattern-based classifiers.


Emerging patterns Patterns discovery Data mining Classification Ant colony optimization 


  1. 1.
    Kwasnik, B.H.: The role of classification in knowledge representation and discovery. Libr. Trends 48(1), 22 (1999)Google Scholar
  2. 2.
    Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 30, 451–462 (2000)CrossRefGoogle Scholar
  3. 3.
    Yoon, H.-S., Lee, S.-H., Kim, J.H.: Application of emerging patterns for multi-source bio-data classification and analysis. Advances in Natural Computation, pp. 965–974. Springer, Berlin (2005)CrossRefGoogle Scholar
  4. 4.
    Fan, H., Ramamohanarao, K.: A weighting scheme based on emerging patterns for weighted support vector machines. In: Granular Computing, 2005 IEEE International Conference, IEEE (2005)Google Scholar
  5. 5.
    Wu, G., et al.: The classification prognosis models of hepatitis b virus reactivation based on Bayes and support vector machine after feature extraction of the genetic algorithm. In: Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016 12th International Conference, IEEE (2016)Google Scholar
  6. 6.
    de Boves Harrington, P.: Support vector machine classification trees based on fuzzy entropy of classification. Anal. Chim. Acta 954, 14–21 (2017)CrossRefGoogle Scholar
  7. 7.
    Yong, Z., Youwen, L., Shixiong, X.: An improved KNN text classification algorithm based on clustering. J. Comput. 4(3), 230–237 (2009)Google Scholar
  8. 8.
    Gokgoz, E., Subasi, A.: Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed. Signal Process. Control 18, 138–144 (2015)CrossRefGoogle Scholar
  9. 9.
    Samantaray, S., ACHLERKAR, P., Manikandan, M.S.: Variational mode decomposition and decision tree based detection and classification of powerquality disturbances in grid-connected distributed generation system (2016)Google Scholar
  10. 10.
    Guan, S.-U., Zhu, F.: An incremental approach to genetic-algorithms-based classification. IEEE Trans. Syst. Man Cybern Part B 35(2), 227–239 (2005)CrossRefGoogle Scholar
  11. 11.
    Enee, G., Escazut C.: Classifier systems evolving multi-agent system with distributed elitism. In: Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress, IEEE (1999)Google Scholar
  12. 12.
    Keshavarz, H., Abadeh, M.S.: SubLex: Generating subjectivity lexicons using genetic algorithm for subjectivity classification of big social data. In: Swarm Intelligence and Evolutionary Computation (CSIEC), 2016 1st Conference, IEEE (2016)Google Scholar
  13. 13.
    Adeniyi, D., Wei, Z., Yongquan, Y.: Automated web usage data mining and recommendation system using K-nearest neighbor (KNN) classification method. Appl. Comput. Inform. 12(1), 90–108 (2016)CrossRefGoogle Scholar
  14. 14.
    Khashei, M., Hejazi, S.R., Bijari, M.: A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets Systems 159(7), 769–786 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Basha, S.H., Abdalla, A.S. Hassanien, A.E.: GNRCS: hybrid classification system based on neutrosophic logic and genetic algorithm. In: Computer Engineering Conference (ICENCO), 2016 12th International, IEEE (2016)Google Scholar
  16. 16.
    MohammadZadeh, J.: Social networks classification using DBN neural network based on genetic algorithm. Social Networks (2016)Google Scholar
  17. 17.
    Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (1999)Google Scholar
  18. 18.
    Fan, H., Ramamohanarao, K.: Noise tolerant classification by chi emerging patterns. Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin (2004)Google Scholar
  19. 19.
    Li, J., Ramamohanarao, K., Dong, G.: The space of jumping emerging patterns and its incremental maintenance algorithms. In: ICML (2000)Google Scholar
  20. 20.
    Ramamohanarao, K., Bailey, J., Fan, H.: Efficient mining of contrast patterns and their applications to classification. In: Intelligent Sensing and Information Processing, 2005. ICISIP 2005. Third International Conference, IEEE (2005)Google Scholar
  21. 21.
    Fan, H., Ramamohanarao, K.: Fast discovery and the generalization of strong jumping emerging patterns for building compact and accurate classifiers. IEEE Trans. Knowl. Data Eng. 18(6), 721–737 (2006)CrossRefGoogle Scholar
  22. 22.
    Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the First European Conference on Artificial Life, Paris (1991)Google Scholar
  23. 23.
    Zhang, X., Dong, G., Kotagiri, R.: Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (2000)Google Scholar
  24. 24.
    Li, J., et al.: Deeps: a new instance-based lazy discovery and classification system. Mach. Learn. 54(2), 99–124 (2004)CrossRefzbMATHGoogle Scholar
  25. 25.
    Wang, Z., Fan, H., Ramamohanarao, K.: Exploiting maximal emerging patterns for classification. Australasian Joint Conference on Artificial Intelligence. Springer, Berlin (2004)Google Scholar
  26. 26.
    Podraża, R., Tomaszewski, K.: KTDA: emerging patterns based data analysis system. Ann. UMCS Sect. AI Inform. 4(1), 279–290 (2006)Google Scholar
  27. 27.
    Alhammady, H.: A novel approach for mining emerging patterns in data streams. In: Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium, IEEE (2007)Google Scholar
  28. 28.
    Ceci, M., Appice, A., Malerba, D.: Discovering emerging patterns in spatial databases: a multi-relational approach. Knowledge Discovery in Databases: PKDD 2007, pp. 390–397. Springer, Berlin (2007)CrossRefGoogle Scholar
  29. 29.
    Ceci, M., Appice, A., Malerba, D.: Emerging pattern based classification in relational data mining. Database and Expert Systems Applications. Springer, Berlin (2008)Google Scholar
  30. 30.
    Poezevara, G., Cuissart, B., Crémilleux, B.: Discovering emerging graph patterns from chemicals. Foundations of Intelligent Systems, pp. 45–55. Springer, Berlin (2009)CrossRefGoogle Scholar
  31. 31.
    Gu, T., et al.: epsicar: an emerging patterns based approach to sequential, interleaved and concurrent activity recognition. In: Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference, IEEE (2009)Google Scholar
  32. 32.
    Chen, X., Lu, L.: An improved algorithm of mining Strong Jumping Emerging Patterns based on sorted SJEP-Tree. In: Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010 IEEE Fifth International Conference, IEEE (2010)Google Scholar
  33. 33.
    Li, H.-F., Chen, H.-S.: Discovering emerging melody patterns from customer query data streams of music service. In: Multimedia and Expo (ICME), 2011 IEEE International Conference, IEEE (2011)Google Scholar
  34. 34.
    Muyeba, M.K., et al.: A framework to mine high-level emerging patterns by attribute-oriented induction. International Conference on Intelligent Data Engineering and Automated Learning. Springer, Berlin (2011)Google Scholar
  35. 35.
    Liu, Q., et al.: A novel approach of mining strong jumping emerging patterns based on BSC-tree. Int. J. Syst. Sci. 45(3), 598–615 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Parmar, H., Chand, C.: Improved high growth-rate emerging pattern based classification. Int. J. Comput. Sci. Mob. Comput. 4, 479–490 (2015)Google Scholar
  37. 37.
    Gambin, T., Walczak, K.: Classification based on the highest impact jumping emerging patterns. In: Computer Science and Information Technology, 2009. IMCSIT’09. International Multiconference, IEEE (2009)Google Scholar
  38. 38.
    Vyas, Z.V., et al.: Modified RAAT (reduced Apriori Algorithm using tag) for efficiency improvement with EP (emerging patterns) and JEP (Jumping EP). In: Advances in Computer Engineering (ACE), 2010 International Conference, IEEE (2010)Google Scholar
  39. 39.
    García-Borroto, M., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.: A New emerging pattern mining algorithm and its application in supervised classification. Advances in Knowledge Discovery and Data Mining, pp. 150–157. Springer, Berlin (2010)CrossRefGoogle Scholar
  40. 40.
    García-Borroto, M., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.: Cascading an emerging pattern based classifier. Advances in Pattern Recognition, pp. 240–249. Springer, Berlin (2010)CrossRefGoogle Scholar
  41. 41.
    Wang, L., Wang, Y., Zhao, D.: Building emerging pattern (EP) random forest for recognition. In: Image Processing (ICIP), 2010 17th IEEE International Conference, IEEE (2010)Google Scholar
  42. 42.
    García-Borroto, M., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.: Fuzzy emerging patterns for classifying hard domains. Knowl. Inf. Syst. 28(2), 473–489 (2011)CrossRefGoogle Scholar
  43. 43.
    Yu, H.-H., Chen, C.-H., Tseng, V.S.: Mining emerging patterns from time series data with time gap constraint. Int. J. Innov. Comput. Inf. Control 7(9), 5515–5528 (2011)Google Scholar
  44. 44.
    Yu, K., et al.: Mining emerging patterns by streaming feature selection. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (2012)Google Scholar
  45. 45.
    Dorigo, M.: Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano, Italy (1992)Google Scholar
  46. 46.
    Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Trans. Evol. Comput. 6(4), 321–332 (2002)CrossRefzbMATHGoogle Scholar
  47. 47.
    Liu, B., Abbass, H.A., McKay, B.: Density-based heuristic for rule discovery with ant-miner. In: The 6th Australia-Japan Joint Workshop on Intelligent and Evolutionary System (2002)Google Scholar
  48. 48.
    Liu, B., Abbass, H.A., McKay, B.: Classification rule discovery with ant colony optimization. In: IAT (2003)Google Scholar
  49. 49.
    Martens, D., et al.: Classification with ant colony optimization. IEEE Trans. Evol. Comput. 11(5), 651–665 (2007)CrossRefGoogle Scholar
  50. 50.
    Baig, A.R., Shahzad, W.: A correlation-based ant miner for classification rule discovery. Neural Comput. Appl. 21(2), 219–235 (2012)CrossRefGoogle Scholar
  51. 51.
    Shahzad, W., Baig, A.: Hybrid associative classification algorithm using ant colony optimization. Int. J. Innov. Comput. Inf. Control 7(12), 6815–6826 (2011)Google Scholar
  52. 52.
    Otero, F.E., Freitas, E.E., Johnson, C.G.: cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes. International Conference on Ant Colony Optimization and Swarm Intelligence. Springer, Berlin (2008)Google Scholar
  53. 53.
    Alcala-Fdez, J., et al.: KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput. 13(3), 307–318 (2009)CrossRefGoogle Scholar
  54. 54.
    Bay, S.D., et al.: The UCI KDD archive of large data sets for data mining research and experimentation. ACM SIGKDD Explor. Newsl. 2(2), 81–85 (2000)CrossRefGoogle Scholar
  55. 55.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  56. 56.
    Salzberg, S.L.: C4. 5: Programs for machine learning by j. ross quinlan. morgan kaufmann publishers, inc., 1993. Mach. Learn. 16(3), 235–240 (1994)MathSciNetGoogle Scholar
  57. 57.
    Schölkopf, B., et al.: New support vector algorithms. Neural comput. 12(5), 1207–1245 (2000)CrossRefGoogle Scholar
  58. 58.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  59. 59.
    Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. (1998)Google Scholar
  60. 60.
    Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning (1995)Google Scholar
  61. 61.
    Sousa, T., Silva, A., Neves, A.: Particle swarm based data mining algorithms for classification tasks. Parallel Comput. 30(5), 767–783 (2004)CrossRefGoogle Scholar
  62. 62.
    McLachlan, G.: Discriminant Analysis and Statistical Pattern Recognition, vol. 544. Wiley, Hoboken (2004)zbMATHGoogle Scholar
  63. 63.
    Le Cessie, S., Van Houwelingen, J.C.: Ridge estimators in logistic regression. Applied Statistics 41, 191–201 (1992)CrossRefzbMATHGoogle Scholar
  64. 64.
    Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learn. 29(2–3), 103–130 (1997)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.National University of Computer and Emerging SciencesIslamabadPakistan

Personalised recommendations