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                                       Details for article 4 of 6 found articles
 
 
  Maintenance of informative ruler sets for predictions
 
 
Title: Maintenance of informative ruler sets for predictions
Author: Wang, Shyue-Liang
Huang, Kuan-Wei
Wang, Tien-Chin
Hong, Tzung-Pei
Appeared in: Intelligent data analysis
Paging: Volume 11 (2007) nr. 3 pages 279-292
Year: 2007-06-22
Contents: An Informative Rule Set (IRS) is the smallest subset of an association rule set such that it has the same prediction sequence by confidence priority [9]. The problem of maintenance of IRS is a process by which, given a transaction database and its IRS, when the database receives insertion, deletion, or modification, we wish to maintain the IRS as efficiently as possible. Based on the Fast UPdating technique (FUP) [5] for the updating of discovered association rules, we propose here two algorithms to update the discovered IRS when the database is updated by insertion and deletion respectively. Numerical comparisons with the non-incremental informative rule set approach show that our proposed techniques require less computation time, due to less database scanning and less number of candidate rules generated.
Publisher: IOS Press
Source file: Elektronische Wetenschappelijke Tijdschriften
 
 

                             Details for article 4 of 6 found articles
 
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