Routing Attribute Data Mining Based on Rough Set Theory

Routing Attribute Data Mining Based on Rough Set Theory

Yanbing Liu (UEST of China & Chongqing University of Posts and Telecommunications, China), Shixin Sun (UEST, China), Menghao Wang (Chongqing University of Posts and Telecommunications, China) and Hong Tang (Chongqing University of Posts and Telecommunications, China)
DOI: 10.4018/978-1-59904-951-9.ch194
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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.

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