Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Discovering knowledge from large databases using prestored information
Information Systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Mining Knowledge Rules from Databases: A Rough Set Approach
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th 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
An Effective Boolean Algorithm for Mining Association Rules in Large Databases
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
Efficient Mining for Association Rules with Relational Database Systems
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
Mining frequent itemsets in data streams using the weighted sliding window model
Expert Systems with Applications: An International Journal
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Mining least relational patterns from multi relational tables
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
A matrix algorithm for mining association rules
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Profile association rule mining using tests of hypotheses without support threshold
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Applying cluster-based fuzzy association rules mining framework into EC environment
Applied Soft Computing
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Mining multidimensional frequent patterns from relational database
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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In this paper, we examine a new data mining issue of mining association rules from customer databases and transaction databases. The problem is decomposed into two subproblems: identifying all the large itemsets from the transaction database and mining association rules from the customer database and the large itemsets identified. For the first subproblem, we propose an efficient algorithm to discover all the large itemsets from the transaction database. Experimental results show that by our approach, the total execution time can be reduced significantly. For the second subproblem, a relationship graph is constructed according to the identified large itemsets from the transaction database and the priorities of condition attributes from the customer database. Based on the relationship graph, we present an efficient graph-based algorithm to discover interesting association rules embedded in the transaction database and the customer database.