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 Dependence Rules
Data Mining and Knowledge Discovery
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth 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
Data Mining of Association Rules and the Process of Knowledge Discovery in Databases
Industrial Conference on Data Mining: Advances in Data Mining, Applications in E-Commerce, Medicine, and Knowledge Management
Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Building a topic hierarchy using the bag-of-related-words representation
Proceedings of the 11th ACM symposium on Document engineering
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The lift of an association rule is frequently used, both in itself and as a component in formulae, to gauge the interestingness of a rule. The range of values that lift may take is used to standardise lift so that it is more effective as a measure of interestingness. This standardisation is extended to account for minimum support and confidence thresholds. A method of visualising standardised lift, through the relationship between lift and its upper and lower bounds, is proposed. The application of standardised lift as a measure of interestingness is demonstrated on college application data and social questionnaire data. In the latter case, negations are introduced into the mining paradigm and an argument for this inclusion is put forward. This argument includes a quantification of the number of extra rules that arise when negations are considered.