Pattern mining is one of the most important topics in data mining. The core idea is to extract relevant 'nuggets' of knowledge describing parts of a database. However, many traditional (frequent) pattern mining algorithms find patterns in numbers that are much too large to be of practical value: so many 'nuggets' of knowledge are found that they do not combine into a better global understanding of the data. In fact, the number of discovered patterns is often larger than the size of the original database!

To tackle this problem, in recent years many techniques have been developed for finding not all, but useful sets of patterns. The aim of this tutorial is to provide a general, comprehensive overview of the state-of-the-art of mining such high-quality sets of patterns.

In the tutorial, an important focus is on the tasks for which patterns can be mined, and how these tasks can influence both the pattern mining and pattern selection process. We make a distinction between patterns mined in an unsupervised setting, where patterns are intended to provide a description of the data and are often the end result of the mining process, and those mined in a supervised setting, where one usually is interested in the later use of patterns in predictive models. Both classes of problems come with distinctive problems, and allow us to discuss the possibilities and powers of using patterns for both classification and explorative data mining.

The main contributions of our tutorial will be that:

• we give a comprehensive overview of pattern set mining techniques.
• we thoroughly explore how classic machine learning tasks and recent pattern set mining techniques are related.
• we clarify the connections between a wide range of pattern and pattern set discovery algorithms.

The aim of the tutorial is to provide a broad overview of the key ideas studied in recent years; it will provide an overview of references for researchers and data mining practitioners interested in the more in-depth details.