Discovering unexpected and useful patterns in databases is a fundamental data mining task.
In recent years, a trend in data mining has been to design algorithms for discovering …

Itemset mining is an important subfield of data mining, which consists of discovering
interesting and useful patterns in transaction databases. The traditional task of frequent …

In this chapter, we introduce Formal Concept Analysis (FCA) and some of its extensions.
FCA is a formalism based on lattice theory aimed at data analysis and knowledge …

High-utility itemset mining (HUIM) is a popular data mining task with applications in
numerous domains. However, traditional HUIM algorithms often produce a very large set of …

Formal concept analysis (FCA) visualizes formal concepts in terms of a concept lattice.
Usually, it is an NP‐problem and consumes plenty of time and storage space to update the …

Abstract Mining High Utility Itemsets (HUIs) is an important task with many applications.
However, the set of HUIs can be very large, which makes HUI mining algorithms suffer from …

The traditional methods for mining association rules (ARs) include two phrases: mining
frequent itemsets (FIs)/frequent closed itemsets (FCIs)/frequent maximal itemsets (FMIs) and …

There have been several pioneering approaches to the extraction of attribute implications
from a formal context, dating from the 1980's: the one of Guigues and Duquenne based on …

A concept lattice is an ordered structure between concepts. It is particularly effective in
mining association rules. However, a concept lattice is not efficient for large databases …

Linked data (LD) is a rich format increasingly exploited in knowledge discovery from data
(KDD). To that end, LD is typically structured as graph, but can also fit the multi-relational …