On-demand Relational Concept Analysis - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2019

On-demand Relational Concept Analysis

Abstract

Formal Concept Analysis (FCA) and its associated conceptual structures are used to support exploratory search through conceptual navigation. Relational Concept Analysis (RCA) is an extension of Formal Concept Analysis to process relational datasets. RCA and its multiple interconnected structures represent good candidates to support exploratory search in relational datasets, as they are enabling navigation within a structure as well as between the connected structures. However, building the entire structures does not present an efficient solution to explore a small localised area of the dataset, to retrieve the closest alternatives to a given query. In these cases, generating only a concept and its neighbour concepts at each navigation step appears as a less costly alternative. In this paper, we propose an algorithm to compute a concept, and its neighbourhood, in connected concept lattices. The concepts are generated directly from the relational context family, and possess both formal and relational attributes. The algorithm takes into account two RCA scaling operators and it is implemented in the RCAExplore tool.
Fichier principal
Vignette du fichier
main.pdf (591.43 Ko) Télécharger le fichier
2019_ICFCA_On_Demand_slides(4).pdf (14.3 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Format Presentation
Comment Slides of the talk
Loading...

Dates and versions

lirmm-02092140 , version 1 (07-04-2019)

Identifiers

Cite

Alexandre Bazin, Jessie Carbonnel, Marianne Huchard, Giacomo Kahn, Priscilla Keip, et al.. On-demand Relational Concept Analysis. ICFCA 2019 - 15th International Conference on Formal Concept Analysis, Jun 2019, Frankfurt, Germany. pp.155-172, ⟨10.1007/978-3-030-21462-3_11⟩. ⟨lirmm-02092140⟩
313 View
208 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More