Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 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 positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Mining infrequent and interesting rules from transaction records
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
Association mining of dependency between time series using Genetic Algorithm and discretisation
International Journal of Business Intelligence and Data Mining
A new parallel association rule mining algorithm on distributed shared memory system
International Journal of Business Intelligence and Data Mining
Investigating the relationship among self-leadership strategies by association rules mining
International Journal of Business Information Systems
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Association Rule Mining (ARM) technique is one of the popular data mining techniques used to discover knowledge from a database. In this paper, we highlight the subsequent drawbacks to association rule mining without considering the absence of items. We propose a novel approach to mine both positive and negative association rules as rule pairs. Our approach ensures that impacts of negative associations are considered so that the drawbacks identified can be avoided. Our initial experiment results show that mining pairs of association rules invoking negative associations are small in number and easy to be appreciated for its implications for decision-making.