An automatic graph layout procedure to visualize correlated data
Mario Inostroza-Ponta, Regina Esther Berretta, Alexandre S. Mendes, Pablo Moscato · 2006
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.