The algorithms for mining of frequent itemsets appeared in the early 1990s. This problem has an important practical application, so there have appeared a lot of new methods of finding frequent itemsets. The number of existing algorithms complicates choosing the optimal algorithm for a certain task and dataset. Twelve most widely used algorithms for mining of frequent itemsets are analyzed and compared in this article. The authors discuss the capabilities of each algorithm and the features of classes of algorithms. The results of empirical research demonstrate different behavior of classes of algorithms according to certain characteristics of datasets.