Association hierarchy mining and its application for network traffic characterisation
Liu, Bin (2014) Association hierarchy mining and its application for network traffic characterisation. PhD thesis, Queensland University of Technology.
This thesis presents an association rule mining approach, association hierarchy mining (AHM). Different to the traditional two-step bottom-up rule mining, AHM adopts one-step top-down rule mining strategy to improve the efficiency and effectiveness of mining association rules from datasets. The thesis also presents a novel approach to evaluate the quality of knowledge discovered by AHM, which focuses on evaluating information difference between the discovered knowledge and the original datasets. Experiments performed on the real application, characterizing network traffic behaviour, have shown that AHM achieves encouraging performance.
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|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Li, Yuefeng & Tian, Glen|
|Keywords:||Data Mining, Association Rule Mining, Rough Set, Granule Mining, Interestingness Measure, Network Traffic Analysis, Characterizing Network Traffic|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Institution:||Queensland University of Technology|
|Deposited On:||05 Dec 2014 03:27|
|Last Modified:||02 Sep 2015 05:23|
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