Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Parallel mining of association rules with a Hopfield type neural network
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Mining spatial association rules in image databases
Information Sciences: an International Journal
Data mining from 1994 to 2004: an application-orientated review
International Journal of Business Intelligence and Data Mining
Deriving non-redundant approximate association rules from hierarchical datasets
Proceedings of the 17th ACM conference on Information and knowledge management
Image restoration using a modified Hopfield network
IEEE Transactions on Image Processing
Towards group behavioral reason mining
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The research objective is to propose a novel association analysis approach using association reasoning neural networks (ARNN) to discover the association rules from cosmetics purchasing. ARNN is modified from multi-layered perceptron and back-propagation algorithm. The number of association rules is controlled by the rule threshold and the number of hidden units. To explore the possibility of producing useful and meaningful association rules using ARNN, our study uses the practical cosmetics transaction data. The results show (1) the predicted output values of ARNN are close to their desired confidence values, (2) reducing the number of hidden units of ARNN can inhibit the generation of association rules with low support, and (3) ARNN has the ability of discovering the cohesion and expansion commodities and this information could be used to make pricing strategy. Therefore, ARNN could be a promising alternative approach for association analysis.