QUT ePrints

Discovering novel knowledge using granule mining

Liu, Bin, Li, Yuefeng, & Tian, Yu-Chu (2012) Discovering novel knowledge using granule mining. In Lecture Notes in Computer Science [Rough Sets and Current Trends in Computing: 8th International Conference], Springer, Chengdu, China, pp. 380-387.

View at publisher

Abstract

This paper presents an extended granule mining based methodology, to effectively describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other granules, it provides a kind of novel knowledge in databases. We also provide an algorithm to implement the proposed methodology. The experiments conducted to characterize a real network traffic data collection show that the proposed concepts and algorithm are promising.

Impact and interest:

Citation countsare 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:

61 since deposited on 04 May 2012
15 in the past twelve months

Full-text downloadsdisplays 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: 50084
Item Type: Conference Paper
Keywords: Granule mining, rough set, decision rule, association rule
DOI: 10.1007/978-3-642-32115-3_45
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200) > Computation Theory and Mathematics not elsewhere classified (080299)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 (please consult the author).
Deposited On: 04 May 2012 15:43
Last Modified: 16 Feb 2013 06:40

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page