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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Autores principales: | , , , |
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Formato: | Objeto de conferencia |
Lenguaje: | Inglés |
Publicado: |
2006
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Materias: | |
Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/23890 |
Aporte de: |
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I19-R120-10915-23890 |
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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 |
work_keys_str_mv |
AT moscatopablo anautomaticgraphlayoutproceduretovisualizecorrelateddata AT inostrozapontamario anautomaticgraphlayoutproceduretovisualizecorrelateddata AT berrettaregina anautomaticgraphlayoutproceduretovisualizecorrelateddata AT mendesalexandre anautomaticgraphlayoutproceduretovisualizecorrelateddata AT moscatopablo automaticgraphlayoutproceduretovisualizecorrelateddata AT inostrozapontamario automaticgraphlayoutproceduretovisualizecorrelateddata AT berrettaregina automaticgraphlayoutproceduretovisualizecorrelateddata AT mendesalexandre automaticgraphlayoutproceduretovisualizecorrelateddata |
bdutipo_str |
Repositorios |
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1764820466376114176 |