Dataset filtering based association rule updating in small-sized temporal databases

Authors:
Jason J. Jung;Geun-Sik Jo
Affiliations:
Intelligent E-Commerce Systems Laboratory, School of Computer Science and Engineering, Inha University, Incheon, Korea;Intelligent E-Commerce Systems Laboratory, School of Computer Science and Engineering, Inha University, Incheon, Korea
Venue:
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Year:
2005

Citing 9
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Online association rule mining

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns by pattern-growth: methodology and implications

ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique

ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A General Incremental Technique for Maintaining Discovered Association Rules

Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Dataset Filtering Techniques in Constraint-Based Frequent Pattern Mining

Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
How Good Are Association-Rule Mining Algorithms?

ICDE '02 Proceedings of the 18th International Conference on Data Engineering
When to update the sequential patterns of stream data?

PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Association rule mining can uncover the most frequent patterns from large datasets. This algorithm such as Apriori, however, is time-consuming task. In this paper we examine the issue of maintaining association rules from newly streaming dataset in temporal databases. More importantly, we have focused on the temporal databases of which storage are restricted to relatively small sized. In order to deal with this problem, temporal constraints estimated by linear regression is applied to dataset filtering, which is a repeated task deleting records conflicted with these constraints. For conducting experiments, we simulated datasets made by synthetic data generator.