Smoothed multiple binarization : using PQR tree, smoothing, feature vectors and thresholding for matrix reordering

Bruno F. Medina; Willian H. Kawakami; Maressa R. da Silva; Celmar G. da Silva

Smoothed multiple binarization : using PQR tree, smoothing, feature vectors and thresholding for matrix reordering

Bruno F. Medina, Willian H. Kawakami, Maressa R. da Silva, Celmar G. da Silva

Material

ARTIGO

Idioma

Inglês

Nota geral

Agradecimentos: We thank grants #2015/00411-6, #2014/11186-0 and #2015/14854-7 from São Paulo Research Foundation (FAPESP) and grant from Coordination for the Improvement of Higher Education Personnel (CAPES)

Este artigo foi apresentado no evento International Conference on Information Visualisation (IV), 2016

Resumo Abstract: Finding appropriate permutations of rows and columns of a matrix may help users to see hidden patterns in datasets. This paper presents a set of binarization-based matrix reordering algorithms able to reveal some patterns in a quantitative data set. In these algorithms, matrix binarization... Ver mais Abstract: Finding appropriate permutations of rows and columns of a matrix may help users to see hidden patterns in datasets. This paper presents a set of binarization-based matrix reordering algorithms able to reveal some patterns in a quantitative data set. In these algorithms, matrix binarization converts a matrix into a set of binary ones, from which the algorithms calculate desired groups of similar rows and columns. PQR trees provide a linear order of rows and columns that obey these groups as much as possible. These algorithms use mean or median filter as smoothing techniques to minimize data noise in intermediate matrix permutation steps. They also use feature vectors or thresholding for defining binarization thresholds in intermediate steps. Our experiments with synthetic matrices revealed that our algorithms are competitive with other matrix reordering algorithms in terms of quality of reordering (Moore stress) and runtime. We observed that our set of algorithms is suitable to reveal Circumplex pattern with all tested noise ratios, and other data canonical patterns with low noise ratio Ver menos

Nota de informação sobre financiamento

COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP

2014/11186-0; 2015/00411-6; 2015/14854-7

Direito de acesso

Fechado

Assuntos

Algoritmos de reordenação de matrizes

Árvore PQR

Reconhecimento de padrões

Parte de evento

Autoria

Medina, Bruno Figueiredo, 1990- Autor

Kwakami, Willian Hitoshi, 1993- Autor

Silva, Maressa Rodrigues da, 1995- Autor

Silva, Celmar Guimarães da, 1978- Autor

International Conference on Information Visualisation (20. : 2016 : Lisbon)

Sites

DOI: https://doi.org/10.1109/IV.2016.22

Texto completo: https://ieeexplore.ieee.org/document/7557909

Smoothed multiple binarization : using PQR tree, smoothing, feature vectors and thresholding for matrix reordering

Bruno F. Medina, Willian H. Kawakami, Maressa R. da Silva, Celmar G. da Silva

Smoothed multiple binarization : using PQR tree, smoothing, feature vectors and thresholding for matrix reordering

Bruno F. Medina, Willian H. Kawakami, Maressa R. da Silva, Celmar G. da Silva

Fontes

Proceedings of the 20th International Conference on Information Visualisation Piscataway, NJ : Institute of Electrical and Electronics Engineers, 2016. p. 88-93

Terminal de consulta web

Smoothed multiple binarization : using PQR tree, smoothing, feature vectors and thresholding for matrix reordering

Smoothed multiple binarization : using PQR tree, smoothing, feature vectors and thresholding for matrix reordering

Smoothed multiple binarization : using PQR tree, smoothing, feature vectors and thresholding for matrix reordering

Fontes