ABSTRACT
Frequent itemset mining is one of the most active research topics in knowledge discovery from databases. The pioneer work was market basket analysis, especially the task to mine transactional data describing the shopping behavior of customers. Since then, a large number of efficient algorithms were developed. In this chapter we review some of the relevant algorithms and their extensions from itemsets to item sequences.
