An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Hyperlink assessment based on web usage mining
Proceedings of the seventeenth conference on Hypertext and hypermedia
Efficient association rule mining among both frequent and infrequent items
Computers & Mathematics with Applications
Jumping emerging patterns with negation in transaction databases - Classification and discovery
Information Sciences: an International Journal
Association rule and quantitative association rule mining among infrequent items
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
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
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
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
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
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
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
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The focus of this paper is the discovery of negative association rules. Such association rules are complementary to the sorts of association rules most often encountered in literatures and have the forms of X \rightarrow \neg Y or \neg X \rightarrow Y. We present a rule discovery algorithm that finds a useful subset of valid negative rules. In generating negative rules, we employ a hierarchical graph-structured taxonomy of domain terms. A taxonomy containing classification information records the similarity between items. Given the taxonomy, sibling rules, duplicated from positive rules with a couple items replaced, are derived together with their estimated confidence. Those sibling rules that bring big confidence deviation are considered candidate negative rules. Our study shows that negative association rules can be discovered efficiently from large database.