Abstract
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.
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Keywords
- Weighted Graph
- Memetic Algorithm
- Local Search Method
- Quadratic Assignment Problem
- European Bioinformatics Institute
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Inostroza-Ponta, M., Berretta, R., Mendes, A., Moscato, P. (2006). An automatic graph layout procedure to visualize correlated data. In: Bramer, M. (eds) Artificial Intelligence in Theory and Practice. IFIP AI 2006. IFIP International Federation for Information Processing, vol 217. Springer, Boston, MA . https://doi.org/10.1007/978-0-387-34747-9_19
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DOI: https://doi.org/10.1007/978-0-387-34747-9_19
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