Association Rules Mining with Auto-Encoders

Association rule mining (ARM) was first introduced by Agrawal [1] to solve the grocery basket problem, and since then it has found numerous applications in Knowledge Discovery in Database (KDD) problems ranging from financial analysis [2] to medical diagnostics [3]. An association rule (AR) is an implication of the form A C, which can be read as "if antecedent A is true then consequent C must be true", where A and C are sets of different items (itemsets) in a database. An AR is defined by its antecedent, its consequent and two measures [4].The first one is the support, which is the proportion of rows in the dataset where both the antecedent and the consequent appear. The second measure is the confidence, the conditional probability to observe the consequent given an observation of the antecedent. The most widely-used mining strategies Apriori [1] and other exhaustive strategies [5, 6, 7] typically work by first mining frequent itemsets, then combining those itemsets to produce association rules. However, all these algorithms face the same problems: the number of rules they produce increases exponentially with the number of items in the database, and thus it becomes impossible for a human to sort through the rules returned to pick out the best ones [8]. Their execution time also become an issue with massive datasets [8]. Finally, these algorithms need support and confidence thresholds in order to efficiently search through the solution space, and those thresholds need to be carefully chosen: low values can lead to long execution times and an overabundance of rules, while high values cause the algorithm to miss interesting rules.