Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Association Rules: Anti-Skew Algorithms
ICDE '98 Proceedings of the Fourteenth 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
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
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)
EDUA: An efficient algorithm for dynamic database mining
Information Sciences: an International Journal
Incremental mining for temporal association rules for crime pattern discoveries
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
An efficient technique for incremental updating of association rules
International Journal of Hybrid Intelligent Systems
Efficiently Discovering Recent Frequent Items in Data Streams
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Frequent items in streaming data: An experimental evaluation of the state-of-the-art
Data & Knowledge Engineering
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
Identifying streaming frequent items in ad hoc time windows
Data & Knowledge Engineering
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Incremental association mining refers to the maintenance and utilization of the knowledge discovered in the previous mining operations for later association mining. Sliding window filtering (SWF) is a technique proposed to filter false candidate 2-itemsets by segmenting a transaction database into partitions. In this paper, we extend SWF by incorporating previously discovered information and propose two algorithms to boost the performance for incremental mining. The first algorithm FI_SWF (SWF with Frequent Itemset) reuses the frequent itemsets of previous mining task to reduce the number of new candidate itemsets that have to be checked. The second algorithm CI_SWF (SWF with Candidate Itemset) reuses the candidate itemsets from the previous mining task. Experiments show that the new proposed algorithms are significantly faster than SWF.