Statistically sound pattern discovery harnesses the rigour of statistical hypothesis testing to
overcome many of the issues that have hampered standard data mining approaches to …

The field of data mining has become accustomed to specifying constraints on patterns of
interest. A large number of systems and techniques has been developed for solving such …

Large data is challenging for most existing discovery algorithms, for several reasons. First of
all, such data leads to enormous hypothesis spaces, making exhaustive search infeasible …

Data analysis is an inherently iterative process. That is, what we know about the data greatly
determines our expectations, and hence, what result we would find the most interesting. With …

The wide adoption of machine learning approaches in the industry, government, medicine
and science has renewed the interest in interpretable machine learning: many decisions are …

Revealing complex relations between entities (eg, items within or between transactions) is of
great significance for business optimization, prediction, and decision making. Such relations …

We introduce the problem of k-pattern set mining, concerned with finding a set of k related
patterns under constraints. This contrasts to regular pattern mining, where one searches for …

Abstract Machine learning and data mining have become aware that using constraints when
learning patterns and rules can be very useful. To this end, a large number of special …

The goal of constraint-based sequence mining is to find sequences of symbols that are
included in a large number of input sequences and that satisfy some constraints specified by …

Knowledge discovery from data is an inherently iterative process. That is, what we know
about the data greatly determines our expectations, and therefore, what results we would …