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Routing Attribute Data Mining Based on Rough Set Theory

Author

Listed:
  • Yanbing Liu

    (UEST of China & Chongqing University of Posts and Telecommunications, China)

  • Shixin Sun

    (UEST of China, China)

  • Menghao Wang

    (Chongqing University of Posts and Telecommunications, China)

  • Hong Tang

    (Chongqing University of Posts and Telecommunications, China)

Abstract

QOSPF(Quality of Service Open Shortest Path First)based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services in the Internet. Data mining has emerged as a tool for data analysis, discovery of new information, and autonomous decision-making. This paper focuses on routing algorithms and their applications for computing QoS routes in OSPF protocol. The proposed approach is based on a data mining approach using rough set theory, for which the attribute-value system about links of networks is created from network topology. Rough set theory of-fers a knowledge discovery approach to extracting routing-decisions from attribute set. The extracted rules can then be used to select significant routing-attributes and make routing-selections in routers. A case study is conducted to demonstrate that rough set theory is effective in finding the most significant attribute set. It is shown that the algo-rithm based on data mining and rough set offers a promising approach to the attribute-selection problem in internet routing.

Suggested Citation

  • Yanbing Liu & Shixin Sun & Menghao Wang & Hong Tang, 2006. "Routing Attribute Data Mining Based on Rough Set Theory," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 2(3), pages 27-41, July.
    • Handle: RePEc:igg:jdwm00:v:2:y:2006:i:3:p:27-41
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