Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Distance Matrix Based Clustering of the Self-Organizing Map
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
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
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This work proposes a theoretical guideline in the specific area of Frequent Itemset Mining (FIM). It supports the hypothesis that the use of neural network technology for the problem of Association Rule Mining (ARM) is feasible, especially for the task of generating frequent itemsets and its variants (e.g. Maximal and closed). We define some characteristics which any neural network must have if we would want to employ it for the task of FIM. Principally, we interpret the results of experimenting with a Self-Organizing Map (SOM) for this specific data mining technique.