An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Borders: An Efficient Algorithm for Association Generation in Dynamic Databases
Journal of Intelligent Information Systems
Data Mining in Large Databases Using Domain Generalization Graphs
Journal of Intelligent Information Systems
An efficient approach to discovering knowledge from large databases
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh 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
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Interesting Association Rules: A Data Mining Language
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A Customer Purchase Incidence Model Applied to Recommender Services
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Hiding Sensitive Association Rules with Limited Side Effects
IEEE Transactions on Knowledge and Data Engineering
Mining association rules in very large clustered domains
Information Systems
Evaluating generalized association rules through objective measures
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Efficient mining of interesting weighted patterns from directed graph traversals
Integrated Computer-Aided Engineering
The Studies of Mining Frequent Patterns Based on Frequent Pattern Tree
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining Frequent Purchase Behavior Patterns for Commercial Websites
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Efficient discovery of multilevel spatial association rules using partitions
Information and Software Technology
Evolutionary approach for mining association rules on dynamic databases
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Rule-chain incremental mining algorithm based on directed graph
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
An efficient graph-based approach to mining association rules for large databases
International Journal of Intelligent Information and Database Systems
Mining Web navigation patterns with a path traversal graph
Expert Systems with Applications: An International Journal
GMA: an approach for association rules mining on medical images
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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Mining association rules is an important task for knowledge discovery. We can analyze past transaction data to discover customer behaviors such that the quality of business decision can be improved. Various types of association rules may exist in a large database of customer transactions. The strategy of mining association rules focuses on discovering large itemsets, which are groups of items which appear together in a sufficient number of transactions. In this paper, we propose a graph-based approach to generate various types of association rules from a large database of customer transactions. This approach scans the database once to construct an association graph and then traverses the graph to generate all large itemsets. Empirical evaluations show that our algorithms outperform other algorithms which need to make multiple passes over the database.