Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and 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
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Materialized Views in Data Mining
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Updating of Association Rules Dynamically
DANTE '99 Proceedings of the 1999 International Symposium on Database Applications in Non-Traditional Environments
An Adaptive Algorithm for Incremental Mining of Association Rules
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Maintenance of generalized association rules with multiple minimum supports
Intelligent Data Analysis
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
Finding association rules in semantic web data
Knowledge-Based Systems
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
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Mining generalized association rules among items in the presence of taxonomies has been recognized as an important model for data mining. Earlier work on mining generalized association rules, however, required the taxonomies to be static, ignoring the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from one hierarchy tree to another for more suitable classification, abandoned from the taxonomies if they will no longer be produced, or added into the taxonomies as new items. Additionally, the analysts might have to dynamically adjust the taxonomies from different viewpoints so as to discover more informative rules. Under these circumstances, effectively updating the discovered generalized association rules is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called Diff_ET and Diff_ET2, to update the discovered frequent itemsets. Empirical evaluation shows that the proposed algorithms are very effective and have good linear scale-up characteristics.