- Title
- An automatic graph layout procedure to visualize correlated data
- Creator
- Inostroza-Ponta, Mario; Berretta, Regina; Mendes, Alexandre; Moscato, Pablo
- 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
- Publisher
- Springer
- Resource Type
- conference paper
- Date
- 2006
- 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.
- Subject
- graph layout; Quadratic Assignment Problem; Standard & Poor's 100; stock market; correlated data
- Identifier
- uon:2864
- Identifier
- http://hdl.handle.net/1959.13/31920
- Identifier
- ISBN:9780387346540
- Identifier
- ISSN:1571-5736
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