| Vol-2729 urn:nbn:de:0074-2729-8 Copyright © 2020 for the individual papers by the papers' authors. Copyright © 2020 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# FCA4AI 2020
What can FCA do for Artificial Intelligence?

### Proceedings of the 8th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2020)
co-located with 24th European Conference on Artificial Intelligence (ECAI 2020)

### Santiago de Compostela, Spain, August 29, 2020.

Edited by

### Sergei O. Kuznetsov *
Amedeo Napoli **
Sebastian Rudolph ***

* National Research University Higher School of Economics, Faculty of Computer Science, Moscow, Russia
** Université de Lorraine, CNRS, Inria, LORIA, F-54000, Nancy, France
*** Technische Universität, Dresden, Germany

## Table of Contents

- Preface

- Embedding Formal Contexts Using Unordered Composition 7-18
  Esteban Marquer, Ajinkya Kulkarni, Miguel Couceiro
- Improving User's Experience in Navigating Concept Lattices: An Approach Based on Virtual Reality 19-30
  Christian Sacarea, Raul-Robert Zavaczki
- Dihypergraph Decomposition: Application to Closure System Representations 31-44
  Simon Vilmin, Lhouari Nourine
- Closure Structure: a Deeper Insight 45-56
  Tatiana Makhalova, Sergei O. Kuznetsov, Amedeo Napoli
- Towards Polynomial Subgroup Discovery by Means of FCA 57-68
  Aleksey Buzmakov
- Representation of Knowledge Using Different Structures of Concepts 69-74
  Dmitry Palchunov, Gulnara Yakhyaeva
- The Study of the Relationship Between Publications in Social Networks Communities via Formal Concept Analysis 75-80
  Kristina Pakhomova, Alina Belova
- Patterns via Clustering as a Data Mining Tool 81-90
  Lars Lumpe, Stefan Schmidt
- Interval-Based Sequence Mining Using FCA and the NextPriorityConcept Algorithm 91-102
  Salah Eddine Boukhetta, Jérémy Richard, Christophe Demko, Karell Bertet
- Continuous Attributes for FCA-based Machine Learning 103-112
  Dmitry Vinogradov
- Granular Computing and Incremental Classification 113-120
  Xenia Naidenova, Vladimir Parkhomenko
- Concept Lattice and Soft Sets. Application to the Medical Image Analysis 121-128
  Anca Christine Pascu, Laurent Nana, Mayssa Tayachi
- Estimation of Errors Rates for FCA-based Knowledge Discovery 129-140
  Dmitry Vinogradov
- Building a Representation Context Based on Attribute Exploration Algorithms 141-152
  Jaume Baixeries, Victor Codocedo, Mehdi Kaytoue, Amedeo Napoli

2020-10-07: submitted by Sergei O. Kuznetsov, Amedeo Napoli, Sebastian Rudolph metadata incl. bibliographic data published under Creative Commons CC0
2020-11-06: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|