Mining Association Rule from Transactions File
Association rule learning is a popular method for discovering interesting relations between variables in large databases. It was often used in market basket analysis domain. But in fact, it can be implemented in various areas where we want to discover the associations between variables. The association is described by a "IF THEN" rule. The IF part is called "antecedent" of the rule; the THEN part correspond to the "consequent" e.g. IF onions AND potatoes THAN burger ( http://en.wikipedia.org/wiki/Association_rule_learning ) i.e. if a customer buys onions and potatoes then he buys also burger. It is possible to find co-occurrences in the standard attribute - value tables that are handled with the most of the data mining tools. In this context, the rows correspond to the baskets (transactions); the columns correspond to the list of all possible products (items); at the intersection of the row and the column, we have an indicator (true/false or 1/0) which ind