Data Mining and Knowledge Discovery Handbook, 2 Edition part 35. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 16 Frequent Set Mining Bart Goethals Departement of Mathemati1cs and Computer Science University of Antwerp Belgium Summary. Frequent sets lie at the basis of many Data Mining algorithms. As a result hundreds 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. Key words 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 mining of association rules as explained in Chapter 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 comprehensive survey of the most influential algorithms and results. Problem Description The original motivation for searching frequent sets came from the need to analyze so called supermarket transaction data that is to examine customer behavior in terms O. Maimon L. Rokach eds. Data Mining and Knowledge Discovery Handbook 2nd ed. DOI 978-0-387-09823-4_16 Springer Science Business
