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

As one of the most comprehensive machine learning texts around, this book does justice to
the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's …

Перед вами один из самых интересных учебников по машинному обучению–разделу
искусственного интеллекта, изучающего методы построения моделей, способных …

Finding subsets of a dataset that somehow deviate from the norm, ie where something
interesting is going on, is a classical Data Mining task. In traditional local pattern mining …

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life
Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent …

Communities can intuitively be defined as subsets of nodes of a graph with a dense
structure in the corresponding subgraph. However, for mining such communities usually …

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 …

Subgroup discovery is a key data mining method that aims at identifying descriptions of
subsets of the data that show an interesting distribution with respect to a pre-defined target …

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

The discovery of patterns that accurately discriminate one class label from another remains
a challenging data mining task. Subgroup discovery (SD) is one of the frameworks that …