An automatic graph layout procedure to visualize correlated data

• Autores: Moscato, Pablo; Inostroza-Ponta, Mario; Berretta, Regina; Mendes, Alexandre
• Año de publicación: 2006
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; Quadratic Assignment Problem (QAP); hierarchical clustering; Similarity measures; Heuristic methods
• Institución: Universidad Nacional de La Plata

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
IFIP International Conference on Artificial Intelligence in Theory and Practice - Knowledge Acquisition and Data Mining
Red de Universidades con Carreras en Informática (RedUNCI)