Forecasting association rules using existing data sets
IEEE Transactions on Knowledge and Data EngineeringPublished 1 November 2003Open access
S.Y. Sung, Zhao Li, Chye Ling Tan, Peter A. Ng
Citations10
SJR quartileQ4
SJR score0.11
SNIP0.06
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TL;DR
This paper provides a novel model for understanding how the differences between two situations affect the changes of the rules, based on the concept of fine partitioned groups that they call caucuses, and provides a simple technique to get a good estimate of the set of rules for a new situation.
Abstract
10.1109/TKDE.2003.1245284
Keywords
Computer Science
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