Traditional approaches to community detection, as studied by physicists, sociologists, and
more recently computer scientists, aim at simply partitioning the social network graph …

Discriminative pattern mining is one of the most important techniques in data mining. This
challenging task is concerned with finding a set of patterns that occur with disproportionate …

Pattern sampling has been proposed as a potential solution to the infamous pattern
explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns …

Constrained counting and sampling are two fundamental problems in Computer Science
with numerous applications, including network reliability, privacy, probabilistic reasoning …

In the field of exploratory data mining, local structure in data can be described by patterns
and discovered by mining algorithms. Although many solutions have been proposed to …

The pattern mining community has shifted its attention from local pattern mining to pattern
set mining. The task of pattern set mining is concerned with finding a set of patterns that …

Working with exhaustive search on large dataset is infeasible for several reasons. Recently,
developed techniques that made pattern set mining feasible by a general solver with long …

Local search methods can quickly find good quality solutions in cases where systematic
search methods might take a large amount of time. Moreover, in the context of pattern set …

An ever-growing list of human endeavors in a variety of domains results in the generation of
time-series data, ie, data that are time-resolved and measured in equidistant time intervals …

Genetic algorithm (GA) based on evolution principles has found its strong base in pattern set
mining. GA has proved to generate more accurate results when compared to other formal …