Chapter 17 FREQUENT SET MINING Bart Goethals Departement of Mathemati1cs and Computer Science, University of Antwerp, Belgium bart.goethals@ua.ac.be Abstract Frequent sets lie at the basis of many Data Mining algorithms. As a result, hun- dreds of algorithms have been proposed in order to solve the frequent set mining problem. In this chapter, we attempt to survey the most successful algorithms and techniques that try to solve this problem efficiently. Keywords: Frequent Set Mining, Association Rule, Support, Cover, Apriori Introduction Frequent sets play an essential role in many Data Mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers, clusters and many more of which the min- ing of association rules, as explained in Chapter 16 in this volume, is one of the most popular problems. The identification of sets of items, products, symptoms, characteristics, and so forth, that often occur together in the given database, can be seen as one of the most basic tasks in Data Mining. Since its introduction in 1993 by Agrawal et al. (1993), the frequent set mining problem has received a great deal of attention. Hundreds of research papers have been published, presenting new algorithms or improvements to solve this mining problem more efficiently. In this chapter, we explain the frequent set mining problem, some of its variations, and the main techniques to solve them. Obviously, given the huge amount of work on this topic, it is impossible to explain or even mention all proposed algorithms or optimizations. Instead, we attempt to give a compre- hensive survey of the most influential algorithms and results.