CEC Faculty Articles

Title

Incremental Quantitative Rule Derivation by Multidimensional Data Partitioning

Event Date/Location

San Francisco, CA / 2000

Document Type

Article

Date

4-2000

Publication Title

Proceedings 15th International Parallel and Distributed Processing Symposium

ISSN or ISBN

1530-2075

First Page

1857

Last Page

1864

Description

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

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