Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Quest: a project on database mining
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
A New and Versatile Method for Association Generation
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Search of Reliable Exceptions
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Exception Rule Mining with a Relative Interestingness Measure
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Generalization of association rules through disjunction
Annals of Mathematics and Artificial Intelligence
Extraction of association rules based on literalsets
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Tractable reasoning problems with fully-characterized association rules
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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Association rule mining is one of the most popular data mining techniques. Significant work has been done to extend the basic association rule framework to allow for mining rules with negation. Negative association rules indicate the presence of negative correlation between items and can reveal valuable knowledge about examined dataset. Unfortunately, the sparsity of the input data significantly reduces practical usability of negative association rules, even if additional pruning of discovered rules is performed. In this paper we introduce the concept of dissociation rules. Dissociation rules present a significant simplification over sophisticated negative association rule framework, while keeping the set of returned patterns concise and actionable. A new formulation of the problem allows us to present an efficient algorithm for mining dissociation rules. Experiments conducted on synthetic datasets prove the effectiveness of the proposed solution.