Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Incrementally fast updated frequent pattern trees
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
Mining minimal non-redundant association rules using frequent itemsets lattice
International Journal of Intelligent Systems Technologies and Applications
An efficient strategy for mining high utility itemsets
International Journal of Intelligent Information and Database Systems
Interestingness measures for association rules: Combination between lattice and hash tables
Expert Systems with Applications: An International Journal
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In the past, Hong et al. proposed an algorithm to maintain the fast updated frequent pattern tree (FUFP-tree), which was an efficient data structure for association-rule mining. However in the maintenance process, the counts of infrequent items and the IDs of transactions with those items were determined by rescanning all the transactions in the original database. This step might be quite time-consuming depending on the number of transactions in the original database and the number of rescanned items. This study improves that approach by storing 1-items during the maintenance process and based on the properties of FUFP-trees, such that the rescanned items and inserted items are processed more efficiently to reduce execution time. Experimental results show that the improved algorithm needs some more memory to store infrequent 1-items but the performance is better than the original one.