An efficient technique for incremental updating of association rules

Authors:
Tarek F. Gharib;Mohamed Taha;Hamed Nassar
Affiliations:
(Correspd. E-mail: tgharib@asunet.shams.edu.eg) Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt;Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt;Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt
Venue:
International Journal of Hybrid Intelligent Systems
Year:
2008

Citing 16
Cited 1

Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison

ACM SIGKDD Explorations Newsletter
Sliding-window filtering: an efficient algorithm for incremental mining

Proceedings of the tenth international conference on Information and knowledge management
Scalable Algorithms for Association Mining

IEEE Transactions on Knowledge and Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique

ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
A General Incremental Technique for Maintaining Discovered Association Rules

Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
CBW: An Efficient Algorithm for Frequent Itemset Mining

HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 3 - Volume 3
Fast Algorithms for Frequent Itemset Mining Using FP-Trees

IEEE Transactions on Knowledge and Data Engineering
An Efficient Algorithm for Incremental Mining of Association Rules

RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Association mining

ACM Computing Surveys (CSUR)
Maintenance of maximal frequent itemsets in large databases

Proceedings of the 2007 ACM symposium on Applied computing
An Efficient Incremental Algorithm for Frequent Itemsets Mining in Distorted Databases with Granular Computing

WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Twain: Two-end association miner with precise frequent exhibition periods

ACM Transactions on Knowledge Discovery from Data (TKDD)
Incremental mining for temporal association rules for crime pattern discoveries

ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
Enhancing SWF for incremental association mining by itemset maintenance

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

An efficient algorithm for incremental mining of temporal association rules

Data & Knowledge Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent developments in information technology resulted in rapid accumulation of huge amounts of data. Therefore, the task of managing large databases efficiently, in addition to quickly retrieving useful information and mining association rules, has become increasingly important. In this paper, an efficient technique is proposed for updating the discovered association rules in incremental databases. The experimental results show that the proposed algorithm is quite efficient and outperforms other well known algorithms.