Formal concepts and closed itemsets proved to be of big importance for knowledge
discovery, both as a tool for concise representation of association rules and a tool for …

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 …

Identifying key structures from social networks that aims to discover hidden patterns and
extract valuable information is an essential task in the network analysis realm. These …

Formal concept analysis (FCA) is a mathematical theory of data analysis with applications in
many areas. The problem of obtaining a concept lattice of an appropriate size was identified …

The lattice theory under the framework of formal concept analysis has brought mathematical
thinking to knowledge representation and discovery. In this respect, this mathematical …

This study is dedicated to the introduction of a novel method that automatically extracts
potential structural alerts from a data set of molecules. These triggering structures can be …

Pattern mining is one of the most studied fields in data mining. Being mostly motivated by
practitioners, pattern mining algorithms are often based on heuristics and are lacking …

Concept lattices arising from noisy or high dimensional data have huge amount of formal
concepts, which complicates the analysis of concepts and dependencies in data. In this …

In pattern mining, the main challenge is the exponential explosion of the set of patterns.
Typically, to solve this problem, a constraint for pattern selection is introduced. One of the …

In this paper, we propose a hybrid and exploratory knowledge discovery approach for
analyzing metabolomic complex data based on a combination of supervised classifiers …