Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th 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)
A Graph-Based Approach for Discovering Various Types of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining and monitoring evolving data
Handbook of massive data sets
estWin: Online data stream mining of recent frequent itemsets by sliding window method
Journal of Information Science
On-line generation association rules over data streams
Information and Software Technology
Negative Generator Border for Effective Pattern Maintenance
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Incremental sequence-based frequent query pattern mining from XML queries
Data Mining and Knowledge Discovery
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Efficiently maintaining structural associations of semistructured data
PCI'01 Proceedings of the 8th Panhellenic conference on Informatics
Mining the customer's up-to-moment preferences for e-commerce recommendation
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Evolution and maintenance of frequent pattern space when transactions are removed
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Measures for comparing association rule sets
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
An efficient itemset mining approach for data streams
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Rule mining for dynamic databases
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
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We consider the problem of finding association rules ina database with binary attributes. Most algorithms for findingsuch rules assume that all the data is available at the start ofthe data mining session. In practice, the data in the databasemay change over time, with records being added and deleted. Atany given time, the rules for the current set of data are of interest. The naive, and highly inefficient, solution would be torerun the association generation algorithm from scratch followingthe arrival of each new batch of data. This paper describes theBorders algorithm, which provides an efficient method forgenerating associations incrementally, from dynamically changingdatabases. Experimental results show an improved performance ofthe new algorithm when compared with previous solutions to theproblem.