Hyperlink assessment based on web usage mining
Proceedings of the seventeenth conference on Hypertext and hypermedia
BLOSOM: a framework for mining arbitrary boolean expressions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Jumping emerging patterns with negation in transaction databases - Classification and discovery
Information Sciences: an International Journal
Mining conditional patterns in a database
Pattern Recognition Letters
The importance of negative associations and the discovery of association rule pairs
International Journal of Business Intelligence and Data Mining
Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Mining Interesting Infrequent and Frequent Itemsets Based on MLMS Model
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Mining Sequential Patterns with Negative Conclusions
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Efficient Mining of Event-Oriented Negative Sequential Rules
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A technique for mining negative association rules
Proceedings of the 2nd Bangalore Annual Compute Conference
In the Search of NECTARs from Evolutionary Trees
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Mining Both Positive and Negative Impact-Oriented Sequential Rules from Transactional Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Proportional fault-tolerant data mining with applications to bioinformatics
Information Systems Frontiers
Proceedings of the VLDB Endowment
Filtering of web recommendation lists using positive and negative usage patterns
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Mining correct properties in incomplete databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Efficiently finding negative association rules without support threshold
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Mining a complete set of both positive and negative association rules from large databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Generating positive and negative exact rules using formal concept analysis: problems and solutions
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Integrating rough set and genetic algorithm for negative rule extraction
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Association rules induced by item and quantity purchased
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Discovering itemset interactions
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
Mining negative generalized knowledge from relational databases
Knowledge-Based Systems
Transactions on rough sets XII
An XML format for association rule models based on the GUHA method
RuleML'10 Proceedings of the 2010 international conference on Semantic web rules
Generalization of association rules through disjunction
Annals of Mathematics and Artificial Intelligence
Mining interesting infrequent and frequent itemsets based on minimum correlation strength
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Inductive learning of disjointness axioms
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
Expert Systems with Applications: An International Journal
Mining flipping correlations from large datasets with taxonomies
Proceedings of the VLDB Endowment
Positive and negative association rule mining on XML data streams in database as a service concept
Expert Systems with Applications: An International Journal
A formal model for mining fuzzy rules using the RL representation theory
Information Sciences: an International Journal
Efficient Search Methods for Statistical Dependency Rules
Fundamenta Informaticae - Machine Learning in Bioinformatics
Computing Implications with Negation from a Formal Context
Fundamenta Informaticae - Concept Lattices and Their Applications
Tractable reasoning problems with fully-characterized association rules
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
Negative-GSP: an efficient method for mining negative sequential patterns
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
A Framework for Synthesizing Arbitrary Boolean Queries Induced by Frequent Itemsets
International Journal of Knowledge-Based Organizations
Mining high coherent association rules with consideration of support measure
Expert Systems with Applications: An International Journal
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Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items (i.e. absent from transactions). Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very convenient for associative classifiers, classifiers that build their classification model based on association rules. Many other applications would benefit from negative association rules if it was not for the expensive process to discover them. Indeed, mining for such rules necessitates the examination of an exponentially large search space. Despite their usefulness, and while they were referred to in many publications, very few algorithms to mine them have been proposed to date. In this paper we propose an algorithm that extends the support-confidence framework with sliding correlation coefficient threshold. In addition to finding confident positive rules that have a strong correlation, the algorithm discovers negative association rules with strong negative correlation between the antecedents and consequents.