Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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)
Ontology-based context synchronization for ad hoc social collaborations
Knowledge-Based Systems
Contextualized Recommendation Based on Reality Mining From Mobile Subscribers
Cybernetics and Systems
Semantic business process integration based on ontology alignment
Expert Systems with Applications: An International Journal
Reusing ontology mappings for query routing in semantic peer-to-peer environment
Information Sciences: an International Journal
Ontology mapping composition for query transformation on distributed environments
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Component retrieval based on a database of graphs for Hand-Written Electronic-Scheme Digitalisation
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
There have been many kinds of association rule mining (ARM) algorithms, e.g., Apriori and FP-tree, to discover meaningful frequent patterns from a large dataset. Particularly, it is more difficult for such ARM algorithms to be applied for temporal databases which are continuously changing over time. Such algorithms are generally based on repeating time-consuming tasks, e.g., scanning databases. To deal with this problem, in this paper, we propose a constraint graph-based method for maintaining frequent patterns (FP) discovered from the temporal databases. Particularly, the constraint graph, which is represented as a set of constraint between two items, can be established by temporal persistency of the patterns. It means that some patterns can be used to build the constraint graph, when the patterns have been shown in a set of the FP. Two types of constraints can be generated by users and adaptation. Based on our scheme, we find that a large number of dataset has been efficiently reduced during mining process and the gathering information while updating.