Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Depth-first frequent itemset mining in relational databases
Proceedings of the 2005 ACM symposium on Applied computing
CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Research issues in data stream association rule mining
ACM SIGMOD Record
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
A new approach to mine frequent patterns using item-transformation methods
Information Systems
Efficient sanitization of informative association rules
Expert Systems with Applications: An International Journal
ON DATA STRUCTURES FOR ASSOCIATION RULE DISCOVERY
Applied Artificial Intelligence
Maintenance of sanitizing informative association rules
Expert Systems with Applications: An International Journal
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Improved methods for extracting frequent itemsets from interim-support trees
Software—Practice & Experience
Hiding Predictive Association Rules on Horizontally Distributed Data
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Hiding collaborative recommendation association rules on horizontally partitioned data
Intelligent Data Analysis
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
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Mining frequent patterns with an FP-tree avoids costlycandidate generation and repeatedly occurrence frequencychecking against the support threshold. It thereforeachieves better performance and efficiency than Apriori-likealgorithms. However, the database still needs tobe scanned twice to get the FP-tree. This can be verytime-consuming when new data are added to an existingdatabase because two scans may be needed for not only thenew data but also the existing data. This paper presentsa new data structure P-tree, Pattern Tree, and a new technique,which can get the P-tree through only one scan of thedatabase and can obtain the corresponding FP-tree with aspecified support threshold. Updating a P-tree with newdata needs one scan of the new data only, and the existingdata do not need to be re-scanned.