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.

Abstract

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.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

124 since deposited on 05 Dec 2014
54 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 78616
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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page