Speaker : Prof. Dr. Markus Hegland (Mathematical Sciences Institute, Australian National University, Canberra) Abstract: Algorithms for Association Rules Association rules are "if-then rules" with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms following from the development of the Apriori algorithm. In these lectures we will introduce basic concepts of association rule discovery including support, confidence, interestingness, the apriori property, and hierarchical and quantitative association rules. The core of the lectures consists of a review of the most important algorithms for association rule discovery. Some familiarity with concepts like predicates, probability, expectation and random variables is assumed.