Paper Info
Title
Efficient remining of generalized association rules under multiple minimum support refinement
Abstract
Mining generalized association rules among items in the presence of taxonomy and with nonuniform minimum support has been recognized as an important model in the data mining community. In real applications, however, the work of discovering interesting association rules is an iterative process; the analysts have to continuously adjust the constraint of minimum support to discover real informative rules. How to reduce the response time for each remining process thus becomes a crucial issue. In this paper, we examine the problem of maintaining the discovered multi-supported generalized association rules when the multiple minimum support constraint is refined and propose a novel algorithm called RGA_MSR to accomplish the work. By keeping and utilizing the set of frequent itemsets and negative border, and adopting vertical intersection counting strategy, the proposed RGA_MSR algorithm can significantly reduce the computation time spent on rediscovery of frequent itemsets and has very good performance.
Year
DOI
Venue
2005
10.1007/11553939_186
KES (3)
Keywords
Field
DocType
iterative process,data mining community,frequent itemsets,computation time,multiple minimum support refinement,nonuniform minimum support,minimum support,novel algorithm,generalized association rule,efficient remining,multiple minimum support constraint,interesting association rule,association rule,data mining
Data mining,Data modeling,Iterative and incremental development,Computer science,Response time,Association rule learning,Correlation and dependence,Computation,Time response
Conference
Volume
ISSN
ISBN
3683
0302-9743
3-540-28896-1
Citations 
PageRank 
References 
1
0.35
15
Authors
3
Name
Order
Citations
PageRank
Ming-cheng Tseng1736.47
Wen-Yang Lin239935.72
Rong Jeng3162.78