Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/978-0-387-34747-9_19 | ARTIFICIAL INTELLIGENCE IN THEORY AND PRACTICE |
Keywords | Field | DocType |
time series,graph partitioning,hierarchical clustering,quadratic assignment problem | Hierarchical clustering,Memetic algorithm,Data mining,Computer science,Quadratic assignment problem,Graph bandwidth,Artificial intelligence,Graph partition,Cluster analysis,Machine learning,Graph (abstract data type),Graph Layout | Conference |
Volume | ISSN | Citations |
217 | 1571-5736 | 5 |
PageRank | References | Authors |
0.56 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mario Inostroza-Ponta | 1 | 41 | 11.08 |
| Regina Berretta | 2 | 49 | 11.60 |
| Alexandre Mendes | 3 | 163 | 18.23 |
| Pablo Moscato | 4 | 334 | 37.27 |