CCE Faculty Articles
Incremental Quantitative Rule Derivation by Multidimensional Data Partitioning
Document Type
Article
Publication Title
Proceedings 15th International Parallel and Distributed Processing Symposium
Event Date/Location
San Francisco, CA / 2000
ISSN
1530-2075
Publication Date
4-2000
Abstract
By using cardinality and relevance information about a set of attributes and concept hierarchies, a top-down incremental data partitioning method is proposed for quantitative rule derivation from database in parallelism. Based on sequential incremental approach, we proposed two parallel versions of incremental partitioning algorithms. These two parallel algorithms are multidimensional-based to partition data set into multiple independent subsets for further rule derivation process. The second version of the parallel algorithm improves the first in terms of load balance.
DOI
10.1109/IPDPS.2001.925145
First Page
1857
Last Page
1864
NSUWorks Citation
Sun, Junping, "Incremental Quantitative Rule Derivation by Multidimensional Data Partitioning" (2000). CCE Faculty Articles. 490.
https://nsuworks.nova.edu/gscis_facarticles/490