14.1 INTRODUCTION TO ASSOCIATION RULES
Association rules seek to uncover associations among the variables and take the form “If antecedent, then consequent,” along with a measure of the support and confidence associated with the rule. For example, a particular supermarket may find that of the 1000 customers shopping on a Thursday night, 200 bought diapers, and of the 200 who bought diapers, 50 bought beer. Thus, the association rule would be: “If buy diapers, then buy beer,” with a support of 50/1000 = 5% and a confidence of 50/200 = 25%.
The daunting problem that awaits any such algorithm is the curse of dimensionality: The number of possible association rules grows exponentially in the number of attributes. Specifically, if there are k attributes, we limit ourselves to binary attributes, we account only for the positive cases (e.g. buy diapers = yes), there are on the order of k ∙ 2k − 1 possible association rules...