Using Modularity with Rough Information Systems

  • Ahmed T. Shawky
  • Hesham A. Hefny
  • Ashraf H. Abd Elwhab
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


We are looking forward to propose a novel technique, which depends on using modular techniques and integration between fuzzy set concepts and rough set theory in mining rough systems. In this research We propose a set of algorithms For a novel model allows introducing modularity mechanism; by introduce decision grouping mechanism for getting the optimizing decision. This approach provides flexibility in decision making verifies all decision standards and determines decision requirements, through modularizing rough information system, extraction of rough association rules and developing mechanisms for decision grouping.


Rough sets Fuzzy sets modularity Data mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Degang, C., Suyun, Z.: Local reduction of decision system with fuzzy rough sets. Fuzzy Sets and Systems 161, 1871–1883 (2010)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Thangavel, K., Pethalakshmi, A.: Dimensionality reduction based on rough set theory. Applied Soft Computing 9, 1–12 (2009)CrossRefGoogle Scholar
  3. 3.
    Qian, J., Liang, D., Li, Z.H., Dang, C.: Measures for evaluating the decision performance of a decision table in rough set theory. Information Sciences 178, 181–202 (2008)CrossRefMATHGoogle Scholar
  4. 4.
    Tseng, B.: Modular neural networks with applications to pattern profiling problems. Neurocomputing (2008)Google Scholar
  5. 5.
    Melin, P., Gonzalez, C., Bravo, D., Gonzalez, F., Martinez, G.: Modular Neural Networks and Fuzzy Sugeno Integral for Pattern Recognition. STUDFUZZ, vol. 208, pp. 311–326 (2007)Google Scholar
  6. 6.
    Grzymala, J.W., Siddhaye, S.: Rough Set Approaches to Rule Induction from Incomplete Data. In: The 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Perugia, Italy, July 4-9, vol. 2, pp. 923–930 (2004)Google Scholar
  7. 7.
    Pedryez, W., Gomide, F.: An Introduction to Fuzzy Sets Analysis and Design. Massaachusetts Institute of Technology (1998)Google Scholar
  8. 8.
    Lefteri, H.T., Robert, E.U.: Fuzzy and Neural Approaches in Engineering. A Wiley-Interscience Publication (1997)Google Scholar
  9. 9.
    Zadeh, L.: Fuzzy Sets. Information and Control 8, 338–353 (1965)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Ahmed T. Shawky
    • 1
  • Hesham A. Hefny
    • 1
  • Ashraf H. Abd Elwhab
    • 2
  1. 1.Computer Sciences and Information Department, Institute of Statistics and ResearchCairo UniversityCairoEgypt
  2. 2.Computer Sciences and Systems DepartmentElectronics Research InstituteCairoEgypt

Personalised recommendations