Many real‐world networks are intimately organized according to a community structure.
Much research effort has been devoted to develop methods and algorithms that can …

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 …

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 …

The relationship between constraint-based mining and constraint programming is explored
by showing how the typical constraints used in pattern mining can be formulated for use in …

We present a method for detecting communities in bipartite networks. Based on an
extension of the k-clique community detection algorithm, we demonstrate how modular …

We present an interpretable machine learning model for medical diagnosis called sparse
high-order interaction model with rejection option (SHIMR). A decision tree explains to a …

Set pattern discovery from binary relations has been extensively studied during the last
decade. In particular, many complete and efficient algorithms for frequent closed set mining …

The support-confidence framework is the most common measure used in itemset mining
algorithms, for its antimonotonicity that effectively simplifies the search lattice. This …

Detecting packer programs is a key step in the defense against malicious programs and
plays a key role in cyber security defenses. One challenge with packer classification is that …

Frequent itemset mining (FIM) is a widely used task that extracts frequently occurring
itemsets from data. Plenty of deterministic algorithms are available for this daunting task …