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Clustering of the Self-Organizing Time Map

Peter Sarlin, Zhiyuan Yao, Clustering of the Self-Organizing Time Map. TUCS Technical Reports 1062, TUCS, 2012.

Abstract:

This paper extends the use of the recently introduced Self-Organizing Time Map (SOTM) by pairing it with classical cluster analysis. The SOTM is an adaptation of the Self-Organizing Map for exploratory temporal structure analysis. While enabling visual dynamic clustering of temporal and cross-sectional patterns, the stand-alone SOTM lacks means for objectively representing temporal changes in cluster structures. This paper combines the SOTM with clustering, and illustrates the usefulness of second-level clustering for representing changes in cluster structures in an easily interpretable format. This provides means for identification of changing, emerging and lost clusters over time. Experiments are performed on two toy datasets and two real-world datasets. The first real-world application explores evolution dynamics of European banks before and during the global financial crisis. Not surprisingly, the results indicate a build-up of risks and vulnerabilities throughout the European banking sector prior to the start of the crisis. The second application identifies the cyclicality of currency crises through changes in the most vulnerable clusters.

Files:

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BibTeX entry:

@TECHREPORT{tSaYa12a,
  title = {Clustering of the Self-Organizing Time Map},
  author = {Sarlin, Peter and Yao, Zhiyuan},
  number = {1062},
  series = {TUCS Technical Reports},
  publisher = {TUCS},
  year = {2012},
  keywords = {Self-Organizing Time Map; cluster analysis; visual dynamic clustering},
  ISBN = {978-952-12-2828-5},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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