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 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 …

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 …

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 …

In most databases, it is possible to identify small partitions of the data where the observed
distribution is notably different from that of the database as a whole. In classical subgroup …

Abstract Machine Learning (ML) and Data Mining (DM) build tools intended to help users
solve data‐related problems that are infeasible for “unaugmented” humans. Tools need …

While a number of efficient sequential pattern mining algorithms were developed over the
years, they can still take a long time and produce a huge number of patterns, many of which …

Clustering entities over knowledge graphs (KGs) is an asset for explorative search and
knowledge discovery. KG embeddings have been intensively investigated, mostly for KG …

Subgroup discovery (SD) is an exploratory pattern mining paradigm that comes into its own
when dealing with large real-world data, which typically involves many attributes, of a …