Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in 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
Evolving data into mining solutions for insights
Communications of the ACM - Evolving data mining into solutions for insights
An Adaptive Algorithm for Mining Association Rules on Shared-Memory Parallel Machines
Distributed and Parallel Databases
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Effect of Data Skewness in Parallel Mining of Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
A Framework for Evaluating Knowledge-Based Interestingness of Association Rules
Fuzzy Optimization and Decision Making
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Mining fuzzy temporal patterns from process instances with weighted temporal graphs
International Journal of Data Analysis Techniques and Strategies
Educational Data Mining: a Case Study
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Mining important association rules based on the RFMD technique
International Journal of Data Analysis Techniques and Strategies
An efficient graph-based approach to mining association rules for large databases
International Journal of Intelligent Information and Database Systems
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Association rule mining is applied to large databases to identify product associations. In the resulting large number of rules, interestingness is difficult to determine. Researchers have defined various measures of 'interestingness' such as support, confidence, lift and gain. Support is the probability of occurrence of an item or set of items, and is the most important of these measures, since the other measures are calculated using support. This current research suggests some deficiencies in the support measure and shows it is not consistent with its definition. Because other measures are calculated using support, this may make the other measures inconsistent. The researcher in this study proposes a new measure called normalised support, which is normalisation of general support, in other context-adjusted support or penalised support. Normalised support recommendations can stabilise product sale by product cross-sell promotion. In addition, the usefulness of other measures improves automatically.