Interpretations of Discovered Knowledge in Multidimensional Databases
Li, Yuefeng (2007) Interpretations of Discovered Knowledge in Multidimensional Databases. In IEEE International Conference on Granular Computing, 2007. GRC 2007., 2-4 November 2007, San Jose, USA.
It is a big challenge to guarantee the quality of discovered knowledge in multidimensional databases because of the huge amount of patterns and noises. The essential issue is to provide efficient methods for interpreting meaningful discovered knowledge in databases. This research presents a new technique called granule mining to improve the performance of data mining. Rather than using patterns, it uses granules in different tiers to generalize knowledge in databases. It also provides a mechanism to formally discuss meaningless discovered rules based on relationships between granules in different tiers.
Impact and interest:
Citation countsare sourced monthly fromand 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 theindexing service can be viewed at the linked Google Scholar™ search.
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.
|Item Type:||Conference Paper|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Systems Development Methodologies (080608)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2007 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||05 Aug 2008|
|Last Modified:||29 Feb 2012 23:38|
Repository Staff Only: item control page