Knowledge and Information Systems

, Volume 28, Issue 2, pp 473–489 | Cite as

Fuzzy emerging patterns for classifying hard domains

  • Milton García-BorrotoEmail author
  • José Fco Martínez-Trinidad
  • Jesús Ariel Carrasco-Ochoa
Regular Paper


Emerging pattern–based classification is an ongoing branch in Pattern Recognition. However, despite its simplicity and accurate results, this classification includes an a priori discretization step that may degrade the classification accuracy. In this paper, we introduce fuzzy emerging patterns as an extension of emerging patterns to deal with numerical attributes using fuzzy discretization. Based on fuzzy emerging patterns, we propose a new classifier that uses a novel graph organization of patterns. The new classifier outperforms some popular and state of the art classifiers on several UCI repository databases. In a pairwise comparison, it significantly beats every other single classifier.


Fuzzy emerging patterns Emerging patterns Supervised classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bongard MN (1963) Solution to geological problems with support of recognition programs. Sov Geologia 6: 33–50Google Scholar
  2. 2.
    Keilis-Borok A, Soloviov A (1991) Pattern recognition: general description. In: Workshop in non-linear dynamics and earthquake prediction, International Center for Science and High Technology, Trieste, Italy, pp 1–14Google Scholar
  3. 3.
    Michalski RS, Stepp R (1982) Revealing conceptual structure in data by inductive inference. In: Michie D, Hayes JE, Pao HH (eds) Machine Intelligence, vol. 10. Ellis Horwood Ltd, New York, , pp 173–196Google Scholar
  4. 4.
    Dong G, Li J (1999) Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, California, United States, ACM, pp 43–52Google Scholar
  5. 5.
    Ramamohanarao K, Fan H (2007) Patterns based classifiers. World Wide Web 10(1): 71–83CrossRefGoogle Scholar
  6. 6.
    Li J, Dong G, Ramamohanarao K (2000) Instance-based classification by emerging patterns. In: Proceedings of the 4th European conference on principles of data mining and knowledge discovery. Springer, pp 191–200Google Scholar
  7. 7.
    Dong G, Zhang X, Wong L, Li J (1999) Caep: classification by aggregating emerging patterns. In: DS’99, vol. 1721 of Lecture Notes in Computer Science, JapanGoogle Scholar
  8. 8.
    Hämälïnen W (2009) Statapriori: an efficient algorithm for searching statistically significant association rules. Knowl Inf Syst. doi: 10.1007/s10115-009-0229-8
  9. 9.
    Jin R, Breitbart Y, Muoh C (2009) Data discretization unification. Knowl Inf Syst 19: 1–29CrossRefGoogle Scholar
  10. 10.
    Fan H, Ramamohanarao K (2006) Fast discovery and the generalization of strong jumping emerging patterns for building compact and accurate classifiers. IEEE Trans Knowl Data Eng 18(6): 721–737CrossRefGoogle Scholar
  11. 11.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8: 338–353MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Weng C-H, Chen Y-L (2009) Mining fuzzy association rules from uncertain data. Knowl Inf Syst. doi: 10.1007/s10115-009-0223-1
  13. 13.
    González A, Pérez R (1999) A study about the inclusion of linguistic hedges in a fuzzy rule learning algorithm. Int J uncertain fuzziness knowl based syst 7(3): 257–266CrossRefGoogle Scholar
  14. 14.
    Fan H, Ramamohanarao K (2002) An efficient single-scan algorithm for mining essential jumping emerging patterns for classification. In: Proceedings of the 6th Pacific-Asia conference on advances in knowledge discovery and data mining, Springer, pp 456–462Google Scholar
  15. 15.
    Fayyad U, Irani K (1993) Multi-interval discretization of continuous-valued attributes for classification learning. In: 13th Int’l Joint Conf Artif Intell (IJCAI), pp 1022–1029Google Scholar
  16. 16.
    Appice A, Ceci M, Malgieri C, Malerba D (2007) Discovering relational emerging patterns. In: AI*IA 2007: artificial intelligence and human-oriented computing, pp 206–217Google Scholar
  17. 17.
    Merz C, Murphy P (1998) Uci repository of machine learning databases. Technical Report, University of California at Irvine, Department of Information and Computer ScienceGoogle Scholar
  18. 18.
    Dasarathy BD (1991) Nearest Neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamitos CaliforniaGoogle Scholar
  19. 19.
    Yuan Y, Shaw M (1995) Induction of fuzzy decision trees. Fuzzy Sets Syst 69: 125–139MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wang XZ, Chen B, Qian G, Ye F (2000) On the optimization of fuzzy decision trees. Fuzzy Sets Syst 112: 117–125MathSciNetCrossRefGoogle Scholar
  21. 21.
    Wang XZ, Zhai JH, Zhang SF (2008) Fuzzy decision tree basen on the important degree of fuzzy attribute. In: 2008 international conference on machine learning and cybernetics, vol. 1, Kunming, pp 511–516Google Scholar
  22. 22.
    Huang D-M (2008) An algorithm for generating fuzzy decision tree with trapezoid fuzzy number-value attributes. In: International conference on wavelet analysis and pattern recognition, ICWAPR 08, vol. 1, Hong Kong, China, pp 41–45Google Scholar
  23. 23.
    Dong M, Kothari R (2001) Look-ahead based fuzzy decision tree induction. IEEE Trans Fuzzy Syst 9(3): 461–468CrossRefGoogle Scholar
  24. 24.
    Wang Z, Fan H, Ramamohanarao K (2004) Exploiting maximal emerging patterns for classification. In: 17th Australian joint conference on artificial intelligence, Cairns, Queensland, Australia, pp 1062–1068Google Scholar
  25. 25.
    Zhang X, Dong G, Ramamohanarao K (2000) Information-based classification by aggregating emerging patterns. In: Proceedings of the second international conference on intelligent data engineering and automated learning, data mining, financial engineering, and intelligent agents, Springer, pp 48–53Google Scholar
  26. 26.
    Fan H, Ramamohanarao K (2003) A bayesian approach to use emerging patterns for classification. In: Proceedings of the 14th Australasian database conference—Volume 17, Adelaide, Australia, pp 39–48, Australian Computer Society, IncGoogle Scholar
  27. 27.
    Bailey J, Manoukian T, Ramamohanarao K (2002) Fast algorithms for mining emerging patterns. In: Proceedings of the 6th European conference on principles of data mining and knowledge discovery, vol. 2431 of Lecture Notes in Computer Sciences, pp 187–208, Springer, 756628 39–50Google Scholar
  28. 28.
    Kuncheva LI. Combining pattern classifiers. Methods and algorithms. Wiley-Interscience, HobokenGoogle Scholar
  29. 29.
    Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8): 832–844CrossRefGoogle Scholar
  30. 30.
    Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann Publishers Inc, San FranciscoGoogle Scholar
  31. 31.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3): 273–297zbMATHGoogle Scholar
  32. 32.
    Wittn I, Frank E, Trigg L, Hall M, Holmes G, Cunnigham S (1999) Weka: practical machine learning tools and techniques with java implementations. In: Emerging knowledge engineering and connectionist-based information systems, pp 192–196Google Scholar
  33. 33.
    Alcalá-Fdez J, Alcalá R, Gacto MJ, Herrera F (2009) Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets Syst 160(7): 905–921zbMATHCrossRefGoogle Scholar
  34. 34.
    Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms, vol. 10. MIT Press, CambridgeGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Milton García-Borroto
    • 1
    Email author
  • José Fco Martínez-Trinidad
    • 2
  • Jesús Ariel Carrasco-Ochoa
    • 2
  1. 1.Centro de BioplantasCiego de AvilaCuba
  2. 2.Instituto Nacional de Astrofísica, Óptica y ElectrónicaPueblaMéxico

Personalised recommendations