Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 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
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Using quantitative information for efficient association rule generation
ACM SIGMOD Record
Parallel data mining for association rules on shared memory systems
Knowledge and Information Systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th 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)
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
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Association rule mining in large databases is one of the most interesting data mining techniques in database communities. In a very large database, the number of discovered rules rise dramatically depending on the selection of support and confidence, and the presentation of the rules in a nice and noticeable way becomes highly challenging. Researchers have developed several tools to visualize association rules in years. However, a large number of tools cannot handle more than dozens of association rules. Furthermore, none of them can effectively manage association rules with multiple antecedents. Till now a uniform descriptive presentation technique has not been set up. We studied different descriptive techniques in the context of visualization and introduced a graph-based technique as Unified Descriptive Language for Association Rules (UDLAR). This unified descriptive language for association rule mining can be used to extract the discovered rules very efficiently.