FCA4AI 2020
What can FCA do for Artificial Intelligence?

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