Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
Mining Text Using Keyword Distributions
Journal of Intelligent Information Systems
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Breaking the barrier of transactions: mining inter-transaction association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The application of association rule mining to remotely sensed data
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Data mining library reuse patterns using generalized association rules
Proceedings of the 22nd international conference on Software engineering
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Targeting the right students using data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
An Experiment in Discovering Association Rules in the Legal Domain
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
SMARViz: Soft Maximal Association Rules Visualization
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
A soft set approach for association rules mining
Knowledge-Based Systems
Automatic specialized vs. non-specialized sentence differentiation
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
TRUMIT: a tool to support large-scale mining of text association rules
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
A two-stage decision model for information filtering
Decision Support Systems
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We describe a new tool for mining association rules, which is of special value in text mining. The new tool, called maximal associations, is geared toward discovering associations that are frequently lost when using regular association rules. Intuitively, a maximal association rule $${X}\stackrel{\rm max}{\Longrightarrow}{Y}$$ says that whenever X is the only item of its type in a transaction, than Y also appears, with some confidence. Maximal associations allow the discovery of associations pertaining to items that most often do not appear alone, but rather together with closely related items, and hence associations relevant only to these items tend to obtain low confidence. We provide a formal description of maximal association rules and efficient algorithms for discovering all such associations. We present the results of applying maximal association rules to two text corpora.