A fast feature vector approach for revealing simplex and equi-correlation data patterns in reorderable matrices
Celmar Guimarães da Silva, Bruno Figueiredo Medina, Maressa Rodrigues da Silva, Willian Hitoshi Kawakami, Miguel Mechi Naves Rocha
ARTIGO
Inglês
Agradecimentos: The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by the São Paulo Research Foundation (FAPESP) (grant numbers #2014/11186-0, #2015/00411-6 and #2015/14854-7), by National...
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Agradecimentos: The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by the São Paulo Research Foundation (FAPESP) (grant numbers #2014/11186-0, #2015/00411-6 and #2015/14854-7), by National Council for Scientific and Technological Development (CNPq) (grant number 123354/2015-3) and also by CAPES
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Abstract: Reorderable matrices may be used as support for tabular displays such as heatmaps. Matrix reordering algorithms provide an initial permutation of these matrices, which should help to reveal hidden patterns in the dataset in the visual structure. Some of these algorithms directly permute...
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Abstract: Reorderable matrices may be used as support for tabular displays such as heatmaps. Matrix reordering algorithms provide an initial permutation of these matrices, which should help to reveal hidden patterns in the dataset in the visual structure. Some of these algorithms directly permute the data matrix, instead of its row- and column-proximity matrices. We present a data matrix reordering method (feature vector-based sort – FVS), which reorders a data matrix aiming to reveal simplex and equi-correlation patterns. Our approach extracts feature vectors from a data matrix and uses them to calculate row and column permutations of the data matrix. We used FVS for reordering data matrices of distinct real-world scenarios, in which it revealed those patterns. Our experiments with synthetic matrices revealed that FVS is faster than other known matrix-reordering algorithms and produces results of approximately the same quality (in terms of stress function) when these patterns are hidden in the data matrix. We also present some real-world datasets reordered by our algorithm and discuss the patterns that it uncovers
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FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP
2014/11186-0; 2015/00411-6; 2015/14854-7
COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ
123354/2015-3
Fechado
A fast feature vector approach for revealing simplex and equi-correlation data patterns in reorderable matrices
Celmar Guimarães da Silva, Bruno Figueiredo Medina, Maressa Rodrigues da Silva, Willian Hitoshi Kawakami, Miguel Mechi Naves Rocha
A fast feature vector approach for revealing simplex and equi-correlation data patterns in reorderable matrices
Celmar Guimarães da Silva, Bruno Figueiredo Medina, Maressa Rodrigues da Silva, Willian Hitoshi Kawakami, Miguel Mechi Naves Rocha
Fontes
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Information visualization v. 16, n. 4, p. 261-274, Oct. 2017 |