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.
| Original language | American English |
|---|---|
| Pages (from-to) | 1598-1605 |
| Number of pages | 8 |
| Journal | Proceedings 15th International Parallel and Distributed Processing Symposium |
| DOIs | |
| State | Published - Aug 29 2005 |
Bibliographical note
Publisher Copyright:© 2001 IEEE.
ASJC Scopus Subject Areas
- Hardware and Architecture
- Computer Networks and Communications
Disciplines
- Computer Sciences
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