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
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
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
Incremental maintenance of generalized association rules under taxonomy evolution
Journal of Information Science
Updating generalized association rules with evolving taxonomies
Applied Intelligence
Incremental Mining of Ontological Association Rules in Evolving Environments
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Maintenance of generalized association rules under transaction update and taxonomy evolution
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
| Hi-index | 0.00 |
Mining generalized association rules among items in the presence of taxonomy has been recognized as an important model in data mining. Earlier work on generalized association rules confined the minimum supports to be uniformly specified for all items or items within the same taxonomy level. This constraint would restrain an expert from discovering more interesting but much less supported association rules. In our previous work, we have addressed this problem and proposed two algorithms, MMS_Cumulate and MMS_Stratify. In this paper, we examined the problem of maintaining the discovered multi-supported, generalized association rules when new transactions are added into the original database. We proposed two algorithms, UD_Cumulate and UD_Stratify, which can incrementally update the discovered generalized association rules with non-uniform support specification and are capable of effectively reducing the number of candidate sets and database re-scanning. Empirical evaluation showed that UD_Cumulate and UD_Stratify are 2-6 times faster than running MMS_Cumulate or MMS_Stratify on the updated database afresh.