Association rule learning is a popular approach to extract rules from large databases. Initially intended to transactional data, especially for the market basket analysis, the method can be applied to any binary or binarized data.
In these slides, we show the outline of the approach. We present a basic algorithm to generate association rules from data. We highlight the influence of the settings (minimum support and minimum confidence) for the reduction of the search space, and thus for the reduction of the amount of calculations.
Keywords: association rule, association rules, itemset, frequent itemset, eclat algorithm, support, confidence, lift
Components (Tanagra): A PRIORI, A PRIORI MR, A PRIORI PT, FREQUENT ITEMSETS, SPV ASSOC RULE, SPV ASSOC TREE
Slides: Association rule learning
References:
Wikipedia, "Association Rule Learning".
M. Zaki, S. Parthasaraty, M. Ogihara, W. Li, “New Algorithms for Fast Discovery of Association Rules”, in Proc. of KDD’97, p. 283-296, 1997.
