An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
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)
A Low-Scan Incremental Association Rule Maintenance Method Based on the Apriori Property
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Efficient incremental maintenance of frequent patterns with FP-tree
Journal of Computer Science and Technology
Incrementally fast updated frequent pattern trees
Expert Systems with Applications: An International Journal
An efficient algorithm for mining frequent closed itemsets in dynamic transaction databases
International Journal of Intelligent Systems Technologies and Applications
An improved data mining approach using predictive itemsets
Expert Systems with Applications: An International Journal
The Pre-FUFP algorithm for incremental mining
Expert Systems with Applications: An International Journal
Maintenance of fast updated frequent pattern trees for record deletion
Computational Statistics & Data Analysis
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Fuzzy data mining based on the compressed fuzzy FP-trees
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Linguistic data mining with fuzzy FP-trees
Expert Systems with Applications: An International Journal
Maintenance of fast updated frequent trees for record deletion based on prelarge concepts
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
An effective tree structure for mining high utility itemsets
Expert Systems with Applications: An International Journal
A new mining approach for uncertain databases using CUFP trees
Expert Systems with Applications: An International Journal
Rule mining for dynamic databases
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
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New transaction insertions and old transaction deletions may lead to previously generated association rules no longer being interesting, and new interesting association rules may also appear. Existing association rules maintenance algorithms are Apriori-like, which mostly need to scan the entire database several times in order to update the previously computed frequent or large itemsets, and in particular, when some previous small itemsets become large in the updated database.This paper presents two new algorithms that use the frequent patterns tree (FP-tree) structure to reduce the required number of database scans. One proposed algorithm is the DB-tree algorithm, which stores all the database information in an FP-tree structure and requires no re-scan of the original database for all update cases. The second algorithm is the PotFp-tree (Potential frequent pattern) algorithm, which uses a prediction of future possible frequent itemsets to reduce the number of times the original database needs to be scanned when previous small itemsets become large after database update.