Non-uniform Sampling Methods for Large Itemset Mining

A well-studied problem in data mining is large itemset mining. To address this problem over very large datasets, several approximate algorithms have been introduced, where an important class of such methods relies on sampling. However in the literature, only methods that are based on uniform sampling are investigated. In this paper, first we discuss how different sampling methods can be described using a generic sampling algorithm and study a property desirable for sampling methods. Then we use this property to argue that some non-uniform sampling methods may work better. We accordingly propose methods that sample each transaction proportional to its number of items or proportional to its number of frequent items. Finally, by conducting extensive experiments over real-world datasets, we show that non-uniform sampling methods usually outperform the uniform method.