Paper Info
Title
Maintenance of discovered informative rule sets: incremental insertion
Abstract
We propose here an efficient data-mining algorithm to discover the informative rule set (IRS) when the transaction database is updated, i.e., when a small transaction data set is added to the original database. An IRS is defined as the smallest subset of an association rule set such that it has the same prediction sequence by confidence priority as the association rule set. A top-down level-wise approach for the discovery of IRS on static database has been proposed. Based on the Fast UPdating technique (FUP) for the updating of discovered association rules, we present an algorithm to maintain the discovered IRS, under incremental insertion. Numerical comparison with the non-incremental informative rule set approach is shown to demonstrate that our proposed technique requires less computation time, in terms of number of database scanning, number of candidate rules generated and processing time, to maintain the discovered informative rule set.
Year
DOI
Venue
2003
10.1109/ICSMC.2003.1244242
SMC
Keywords
Field
DocType
database management systems,knowledge based systems,irs,fup,incremental insertion,set theory,top-down level-wise approach,transaction data set,discovered informative rule sets maintenance,computation time,association rule set,data mining,fast updating technique,static database,database scanning,transaction database,nonincremental informative rule set,data mining algorithm,transaction processing,transaction data,top down,association rule
Transaction processing,Data mining,Set theory,Information retrieval,Computer science,Knowledge-based systems,Association rule learning,Database transaction,Transaction data,Computation
Conference
Volume
ISSN
ISBN
3
1062-922X
0-7803-7952-7
Citations 
PageRank 
References 
1
0.41
11
Authors
4
Name
Order
Citations
PageRank
Shyue-liang Wang154850.66
Kuan-Wei Huang211.43
Tien-Chin Wang357430.86
Tzung-pei Hong43768483.06