Interpretation of association rules with multi-tier granule mining

Wu, Jingtong (2014) Interpretation of association rules with multi-tier granule mining. PhD thesis, Queensland University of Technology.

Abstract

This study was a step forward to improve the performance for discovering useful knowledge – especially, association rules in this study – in databases. The thesis proposed an approach to use granules instead of patterns to represent knowledge implicitly contained in relational databases; and multi-tier structure to interpret association rules in terms of granules. Association mappings were proposed for the construction of multi-tier structure. With these tools, association rules can be quickly assessed and meaningless association rules can be justified according to the association mappings. The experimental results indicated that the proposed approach is promising.

Impact and interest:

2 citations in Web of Science®
Search Google Scholar™

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:

87 since deposited on 30 May 2014
18 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: 71455
Item Type: QUT Thesis (PhD)
Supervisor: Li, Yuefeng & Bruza, Peter
Keywords: Data mining, Association rule mining, Granule mining, Decision rules, Rough sets
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 30 May 2014 02:15
Last Modified: 09 Sep 2015 05:45

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page