An automatic graph layout procedure to visualize correlated data

This paper introduces an automatic procedure to assist on the interpretation of a large dataset when a similarity metric is available. We propose a visualization approach based on a graph layout method- ology that uses a Quadratic Assignment Problem (QAP) formulation. The methodology is presented...

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Detalles Bibliográficos
Autores principales: Moscato, Pablo, Inostroza-Ponta, Mario, Berretta, Regina, Mendes, Alexandre
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2006
Materias:
Ciencias Informáticas
Quadratic Assignment Problem (QAP)
hierarchical clustering
Similarity measures
Heuristic methods
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23890
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23890
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Quadratic Assignment Problem (QAP)
hierarchical clustering
Similarity measures
Heuristic methods
spellingShingle Ciencias Informáticas
Quadratic Assignment Problem (QAP)
hierarchical clustering
Similarity measures
Heuristic methods
Moscato, Pablo
Inostroza-Ponta, Mario
Berretta, Regina
Mendes, Alexandre
An automatic graph layout procedure to visualize correlated data
topic_facet Ciencias Informáticas
Quadratic Assignment Problem (QAP)
hierarchical clustering
Similarity measures
Heuristic methods
description This paper introduces an automatic procedure to assist on the interpretation of a large dataset when a similarity metric is available. We propose a visualization approach based on a graph layout method- ology that uses a Quadratic Assignment Problem (QAP) formulation. The methodology is presented using as testbed a time series dataset of the Standard & Poor’s 100, one the leading stock market indicators in the United States. A weighted graph is created with the stocks repre- sented by the nodes and the edges’ weights are related to the correlation between the stocks’ time series. A heuristic for clustering is then pro- posed; it is based on the graph partition into disconnected subgraphs allowing the identification of clusters of highly-correlated stocks. The final layout corresponds well with the perceived market notion of the different industrial sectors. We compare the output of this procedure with a traditional dendogram approach of hierarchical clustering
format Objeto de conferencia
Objeto de conferencia
author Moscato, Pablo
Inostroza-Ponta, Mario
Berretta, Regina
Mendes, Alexandre
author_facet Moscato, Pablo
Inostroza-Ponta, Mario
Berretta, Regina
Mendes, Alexandre
author_sort Moscato, Pablo
title An automatic graph layout procedure to visualize correlated data
title_short An automatic graph layout procedure to visualize correlated data
title_full An automatic graph layout procedure to visualize correlated data
title_fullStr An automatic graph layout procedure to visualize correlated data
title_full_unstemmed An automatic graph layout procedure to visualize correlated data
title_sort automatic graph layout procedure to visualize correlated data
publishDate 2006
url http://sedici.unlp.edu.ar/handle/10915/23890
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