Routing Attribute Data Mining Based on Rough Set Theory

Yanbing Liu, Shixin Sun, Menghao Wang and Hong Tang
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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

International Journal of Data Warehousing and Mining (IJDWM), 2006, vol. 2, issue 3, 27-41

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.

Date: 2006
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