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Authors: Sonia Fiol-González 1 ; Cassio Almeida 2 ; Ariane Rodrigues 1 ; Simone Barbosa 1 and Hélio Lopes 1

Affiliations: 1 Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro and Bazil ; 2 Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro, Bazil, ENCE - Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro and Brazil

ISBN: 978-989-758-354-4

Keyword(s): Clustering, Ensemble Methods, Ensemble Visualization, Uncertainty Visualization, Co-association Matrix.

Related Ontology Subjects/Areas/Topics: Abstract Data Visualization ; Computer Vision, Visualization and Computer Graphics ; General Data Visualization ; Graph Visualization ; Information and Scientific Visualization ; Interface and Interaction Techniques for Visualization ; Visual Data Analysis and Knowledge Discovery

Abstract: Uncertainty Analysis is essential to support decisions, and it has been gaining attention in both visualization and machine learning communities —in the latter case, mainly because ensemble methods are becoming a robust approach in several applications. In particular, for unsupervised learning, there are several ensemble clustering methods that generate a co-association matrix, i.e., a matrix whose element (i, j) represents the estimated probability that the given sample pair is on the same cluster. This work studies the following decision problem: “Given a similarity function, which groups of elements of a set form robust clusters?” Robust here means that all elements of each cluster are connected with a probability within a given interval. Our main contribution is a prototype that helps decision makers, through visual exploration, to have insights to solve this task. To do so, we provide visual tools for ensemble clustering analysis. Such tools are grounded in the co-association mat rix generated by the ensemble. With these tools we are better equipped to recommend the group of elements that form each cluster, considering the uncertainty generated by ensemble clustering methods. (More)

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Paper citation in several formats:
Fiol-González, S.; Almeida, C.; Rodrigues, A.; Barbosa, S. and Lopes, H. (2019). Visual Exploration Tools for Ensemble Clustering Analysis.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3 IVAPP: IVAPP, ISBN 978-989-758-354-4, pages 259-266. DOI: 10.5220/0007366302590266

@conference{ivapp19,
author={Sonia Fiol{-}González. and Cassio F. P. Almeida. and Ariane M. B. Rodrigues. and Simone D. J. Barbosa. and Hélio Lopes.},
title={Visual Exploration Tools for Ensemble Clustering Analysis},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3 IVAPP: IVAPP,},
year={2019},
pages={259-266},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007366302590266},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3 IVAPP: IVAPP,
TI - Visual Exploration Tools for Ensemble Clustering Analysis
SN - 978-989-758-354-4
AU - Fiol-González, S.
AU - Almeida, C.
AU - Rodrigues, A.
AU - Barbosa, S.
AU - Lopes, H.
PY - 2019
SP - 259
EP - 266
DO - 10.5220/0007366302590266

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