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 …

Mining important conceptual patterns is an essential task for understanding the context and
content of complex data in many scientific and engineering applications. While exact …

In this work we propose R-GPM, a parallel computing framework for graph pattern mining
(GPM) through a user-defined subgraph relation. More specifically, we enable the …

Formal concept analysis (FCA) is a mathematical tool for analyzing data and formally
representing conceptual knowledge. Under this formalism, the concept stability metric can …

The effective use of the concept lattice in large datasets has been always limited by the large
volume of extracted knowledge. The stability measure has been shown to be of valuable …

Inherently explainable Machine Learning (ML) models are able to provide explanations for
their predictions by virtue of their construction. The explanations of a ML model are more …

Discovering subgroups with significant association with binary class labels has wide
applications in drug discovery, market basket analysis, etc. The state-of-the-art technique …

Abstract Inherently explainable Machine Learning (ML) models are able to provide
explanations for their predictions by virtue of their construction. The explanations of a ML …

This paper proposes an approach and an associated system based on pattern structures,
aimed at the classification of documents represented as graphs. The representation of …

Clustering aims at finding disjoint groups of similar objects in data and is one major task in
Machine Learning. Yet, it is gaining more attention in Formal Concept Analysis community in …