Information Granules Problem: An Efficient Solution of Real-Time Fuzzy Regression Analysis

  • Azizul Azhar Ramli
  • Junzo WatadaEmail author
  • Witold Pedrycz
Part of the Studies in Big Data book series (SBD, volume 8)


Currently, Big Data is one of the common scenario which cannot be avoided. The presence of the voluminous amount of unstructured and semi-structured data would take too much time and cost too much money to load into a relational database for analysis purpose. Beside that, regression models are well known and widely used as one of the important categories of models in system modeling. This chapter shows an extended version of fuzzy regression concept in order to handle real-time data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GAFCM) and a convex hull-based regression approach, which is regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing with the proposed approach, helps to reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. Additionally, the proposed approach shows an efficient real-time processing of information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub-convex hulls as well as a main convex hull structure. In the proposed design setting, it was emphasized a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.


Granular computing Fuzzy regression analysis Information granules Fuzzy C-means Convex hulls Convex hull Beneath-beyond algorithm 



The first author was worked with Universiti Tun Hussion Onn Malaysia, MALAYSIA and enrolled as PhD Candidate at Graduate School of Information, Production and Systems (IPS), Waseda University, Fukuoka, JAPAN.


  1. 1.
    Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  2. 2.
    Shifei, D., Li, X., Hong, Z., Liwen, Z.: Research and progress of cluster algorithms based on granular computing. Int. J. Digit. Content Technol. Appl. 4(5), 96–104 (2010)CrossRefGoogle Scholar
  3. 3.
    Snijders, C., Matzat, U., Reips, U.-D.: ‘Big data’: Big gaps of knowledge in the field of internet. Int. J. Internet Sci. 7, 1–5 (2012)Google Scholar
  4. 4.
    Hall, L.O., Ozyurt, I.B., Bezdek, J.C.: Clustering with a genetically optimized approach. IEEE Trans. Evol. Comput. 3(2), 103–112 (1999)CrossRefGoogle Scholar
  5. 5.
    Bargiela, A., Pedrycz, W.: Toward a theory of granular computing for human centered information processing. IEEE Trans. Fuzzy Syst. 16(16), 320–330 (2008)CrossRefGoogle Scholar
  6. 6.
    Chen, B., Tai, P.C., Harrison, R., Pan, Y.: FIK model: Novel efficient granular computing model for protein sequence motifs and structure information discovery. In: 6th IEEE International Symposium on BioInformatics and BioEngineering (BIBE 2006), Arlington, Virginia, pp. 20–26 (2006)Google Scholar
  7. 7.
    Ramli, A.A., Watada, J., Pedrycz, W.: Real-time fuzzy regression analysis: A convex hull approach. Eur. J. Oper. Res. 210(3), 606–617 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Ramli A.A., Watada, J.: New perspectives of fuzzy performance assessment of manufacturing enterprises. In: The 5th International. Conference on Intelligent Manufacturing and Logistics Systems (IML 2009), Waseda University, Kitakyushu, Japan, pp. 16–18 (2009)Google Scholar
  9. 9.
    Yu, P.-S., Chena, S.-T., Changa, I.-F.: Support vector regression for real-time flood stage forecasting. J. Hydrol. 328(3–4), 704–716 (2006)CrossRefGoogle Scholar
  10. 10.
    Wang, W., Chena, S., Qu, G.: Incident detection algorithm based on partial least squares regression. Transp. Res. Part C: Emerg. Technol. 16(1), 54–70 (2008)CrossRefGoogle Scholar
  11. 11.
    Pedrycz, W., Vulcovich, G.: Representation and propagation of information granules in rule-based computing. J. Adv. Comput. Intell. Intell. Inf. 4(1), 102–110 (2000)Google Scholar
  12. 12.
    Hoppner F., Klawonn, F.: Systems of information granules. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing. John Wiley & Sons Ltd, Chichester (2008). doi: 10.1002/9780470724163.ch9
  13. 13.
    Chen, B., Hu, J., Duan, L., Gu, Y.: Network administrator assistance system based on fuzzy C-means analysis. J. Adv. Comput. Intell. Intell. Inf. 13(2), 91–96 (2009)Google Scholar
  14. 14.
    Nascimento, S., Mirkin, B., Moura-Pires, F.: A fuzzy clustering model of data and fuzzy C-means. In: IEEE Conference on Fuzzy Systems (FUZZ-IEEE2000), San Antonio, Texas, USA, pp. 302–307 (2000)Google Scholar
  15. 15.
    Alata, M., Molhim, M., Ramini, A.: Optimizing of fuzzy C-means clustering algorithm using GA. World Acad. Sci. Eng. Technol. 224–229 (2008) Google Scholar
  16. 16.
    Yabuuchi, Y., Watada, Y.: Possibilistic forecasting model and its application to analyze the economy in Japan. Lecture Notes in Computer Science, vol. 3215, pp. 151–158. Springer, Berlin, Heidelberg (2004)Google Scholar
  17. 17.
    Lin, H.J., Yang, F.W., Kao, Y.T.: An efficient GA-based clustering technique. Tamkang J. Sci. Eng. 8(2), 113–122 (2005)Google Scholar
  18. 18.
    Wang, Y.: Fuzzy clustering analysis by using genetic algorithm. ICIC Express Lett. 2(4), 331–337 (2008)Google Scholar
  19. 19.
    Emiris, Z.: A complete implementation for computing general dimensional convex hulls. Int. J. Comput. Geometry Appl. 8(2), 223–249 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Barber, B., Dobki, D.P., Hupdanpaa, H.: The quickhull algorithm for convex hull. ACM Trans. Math. Softw. 22(4), 469–483 (1996)CrossRefzbMATHGoogle Scholar
  21. 21.
    Wang, H.-F., Tsaur, R.-C.: Insight of a possibilistic regression model. Fuzzy Sets Syst. 112(3), 355–369 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  22. 22.
    Watada, J., Pedrycz, W.: A possibilistic regression approach to acquisition of linguistic rules. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook on granular commutation, pp. 719–740. John Wiley and Sons Ltd., New York (2008)Google Scholar
  23. 23.
    Ramli, A.A., Watada, J., Pedrycz, W.: An efficient solution of real-time fuzzy regression analysis to information granules problem. J. Adv. Comput. Intell. Intell. Inf. (JACIII) 16(2), 199–209 (2012)Google Scholar
  24. 24.
    Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Trans. Syst. Man Cybern. 12(6), 903–907 (1982)Google Scholar
  25. 25.
    Ramli, A.A., Watada, J., Pedrycz, W. Real-time fuzzy switching regression analysis: A convex hull approach. In: 11th International Conference on Information Integration and Web-based Applications and Services (iiWAS2009), Kuala Lumpur, Malaysia, pp. 284–291 (2009)Google Scholar
  26. 26.
    Ramli, A.A., Watada, J., Pedrycz, W.: A combination of genetic algorithm-based fuzzy C-means with a convex hull-based regression for real-time fuzzy switching regression analysis: Application to industrial intelligent data analysis. IEEJ Transactions on Electr. Electron. Eng. 9(1), 71–82 (2014)CrossRefGoogle Scholar
  27. 27.
    Frank A., Asuncion, A.: UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA. (2010)

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Azizul Azhar Ramli
    • 1
  • Junzo Watada
    • 2
    Email author
  • Witold Pedrycz
    • 3
    • 4
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaParit Raja, Batu PahatMalaysia
  2. 2.Graduate School of Information, Production and SystemsWaseda UniversityKitakyushu-shiJapan
  3. 3.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  4. 4.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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