Pattern Recognition for MCNs Using Fuzzy Linear Programming

  • Jing He
  • Wuyi Yue
  • Yong Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)


This paper presents a data mining system of performance evaluation for multimedia communication networks (MCNs). Two important performance evaluation problems for the MCNs are considered in this paper. They are: (1) the optimization problem for construction of the data mining system of performance evaluation; (2) the problem of categorizing real-time data corresponding to the data mining system by means of dividing the performance data into usual and unusual categories. An algorithm is employed to identify performance data such as throughput capacity, package forwarding rate, and response time. A software named PEDM2.0 (Performance Evaluation Data Miner) is proposed to improve the accuracy and the effectiveness of the fuzzy linear programming (FLP) method compared with decision tree, neural network, and multiple criteria linear programming methods.


Data Warehouse Unusual Pattern Throughput Capacity Fuzzy Linear Programming Data Mining System 
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.


  1. 1.
    Yue, W., Gu, J., Tang, X.: Performance evaluation index system for multimedia communication networks and forecasting for web-based network traffic. Journal of System Science and System Engineering 13, 44–50 (1994)Google Scholar
  2. 2.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2003)Google Scholar
  3. 3.
    Freed, N., Glover, F.: Simple but powerful goal programming models for discriminant problems. European Journal of Operation Research 7, 44–60 (1981)MATHCrossRefGoogle Scholar
  4. 4.
    Freed, N., Glover, F.: Evaluating alternative linear programming models to solve the two-group discriminant problem. Journal of Decision Science 17, 151–162 (1986)CrossRefGoogle Scholar
  5. 5.
    Glover, F.: Improve linear programming models for discriminate analysis. Journal of Decision Science 21, 771–785 (1990)CrossRefGoogle Scholar
  6. 6.
    Kou, G., Liu, X., Peng, Y., Shi, Y., Wise, M., Xu, W.: Multiple criteria linear programming approach to data mining: models, algorithm designs and software development. Journal of Operation Methods and Software 18, 453–473 (2003)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Kou, G., Shi, Y.: LINUX based Multiple Linear Programming Classification Program: Version 1.0. College of Information Science and Technology, University of Nebraska-Omaha (2002)Google Scholar
  8. 8.
    Shi, Y., He, J., Wang, L., Fan, W.: Computer-based algorithms for multiple criteria and multiple constraint level integer linear programming. Computers and Mathematics with Applications 49, 903–921 (2005)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    He, J., Yue, W., Shi, Y.: Identification mining of unusual patterns for multimedia communication networks by using fuzzy linear programming. IEICE Technical Report  DE2005-17, 11–17 (2005)Google Scholar
  10. 10.
    He, J., Shi, Y.: Performance Evaluation Data Miner 2.0, CAS Research Center on Data Technology and Knowledge Economy (2005)Google Scholar
  11. 11.
  12. 12.
  13. 13.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jing He
    • 1
    • 3
  • Wuyi Yue
    • 2
  • Yong Shi
    • 3
  1. 1.Institute of Intelligent Information and Communication TechnologyKonan UniversityKobeJapan
  2. 2.Department of Information Science and Systems EngineeringKonan UniversityKobeJapan
  3. 3.Chinese Academy of Sciences Research Center on Data Technology and Knowledge EconomyBeijingP.R. China

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