Frequent item set mining is one of the best known and most popular data mining methods.
Originally developed for market basket analysis, it is used nowadays for almost any task that …

Subgroup discovery is a broadly applicable descriptive data mining technique for identifying
interesting subgroups according to some property of interest. This article summarizes …

One of the most important obstacles to deploying predictive models is the fact that humans
do not understand and trust them. Knowing which variables are important in a model's …

One of the major problems in pattern mining is the explosion of the number of results. Tight
constraints reveal only common knowledge, while loose constraints lead to an explosion in …

Clustering high dimensional data is an emerging research field. Subspace clustering or
projected clustering group similar objects in subspaces, ie projections, of the full space. In …

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 …

Community discovery in complex networks is the problem of detecting, for each node of the
network, its membership to one of more groups of nodes, the communities, that are densely …

Social interactions that occur regularly typically correspond to significant yet often infrequent
and hard to detect interaction patterns. To identify such regular behavior, we propose a new …

We present smart drill-down, an operator for interactively exploring a relational table to
discover and summarize “interesting” groups of tuples. Each group of tuples is described by …

Mining small, useful, and high-quality sets of patterns has recently become an important
topic in data mining. The standard approach is to first mine many candidates, and then to …