Constrained pattern mining in the new era

Twenty years of research on frequent itemset mining, or pattern mining , has led to the existence of a set of efficient algorithms for identifying different types of patterns, from transactional to sequential. Despite the great advances in this field, big data brought a completely new context to operate, with new challenges arising from the growth in data size, dynamics and complexity. These challenges include the shift not only from static to dynamic data, but also from tabular to complex data sources, such as social networks (expressed as graphs) and data warehouses (expressed as multi-relational models). In this new context, and more than ever, users need effective ways to control the large number of discovered patterns, and to be able to choose what patterns to consider at each time. The most accepted and common approach to minimize these drawbacks has been to capture and represent the semantics of the domain through constraints, and use them not only to reduce the number of results, but also to focus the algorithms in areas where it is more likely to gain information and return more interesting results. The use of constraints in pattern mining has been widely studied, and there are a lot of proposed types of constraints and pushing strategies. In this paper, we present a new global view of the work done on the incorporation of constraints in the pattern mining process. In particular, we propose a new framework for constrained pattern mining, that allows us to organize and analyze existing algorithms and strategies, based on the different types and properties of constraints, and on the data sources they are able to handle.