The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Understanding semantic relationships
The VLDB Journal — The International Journal on Very Large Data Bases
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
Automated support specification for efficient mining of interesting association rules
Journal of Information Science
Maintenance of generalized association rules with multiple minimum supports
Intelligent Data Analysis
A new incremental data mining algorithm using pre-large itemsets
Intelligent Data Analysis
Mining Association Rules with Ontological Information
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
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The process of knowledge discovery from databases is a knowledge intensive, highly user-oriented practice, thus has recently heralded the development of ontology-incorporated data mining techniques. In our previous work, we have considered the problem of mining association rules with ontological information (called ontological association rules) and devised two efficient algorithms, called AROC and AROS, for discovering ontological associations that exploit not only classification but also composition relationship between items. The real world, however, is not static. Data mining practitioners usually are confronted with a dynamic environment. New transactions are continually added into the database over time, and the ontology of items is evolved accordingly. Furthermore, the work of discovering interesting association rules is an iterative process; the analysts need to repeatedly adjust the constraint of minimum support and/or minimum confidence to discover real informative rules. Under these circumstances, how to dynamically discover association rules efficiently is a crucial issue. In this regard, we proposed a unified algorithm, called MIFO, which can handle the maintenance of discovered frequent patterns taking account of all evolving factors: new transactions updating in databases, ontology evolution and minimum support refinement. Empirical evaluation showed that MIFO is significantly faster than running our previous algorithms AROC and AROS from scratch.