Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/31920

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Title
An automatic graph layout procedure to visualize correlated data
Author/Creator
Inostroza-Ponta, Mario;  Berretta, Regina;  Mendes, Alexandre;  Moscato, Pablo
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 methodology 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 represented by the nodes and the edges' weights are related to the correlation between the stocks' time series. A heuristic for clustering is then proposed; 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.
Relation
IFIP 19th World Computer Congress, TC 12: IFIP AI 2006 Stream. Artificial Intelligence in Theory and Practice (Santiago, Chile 21-24 August, 2006) p. 179-188
Publisher Link
http://dx.doi.org/10.1007/978-0-387-34747-9_19
Date
2006
Publisher
Springer
Keyword(s)
graph layout;  Quadratic Assignment Problem;  Standard & Poor's 100;  stock market;  correlated data
Resource Type
conference paper
Identifier
http://hdl.handle.net/1959.13/31920
Identifier
ISBN:9780387346540
Identifier
ISSN:1571-5736
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"An automatic graph layout procedure to visualize correlated data"
 
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