Clustering of the self-organizing time map

Peter Sarlin*, Zhiyuan Yao

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)

Abstract

This paper extends the use of 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 visualizing dynamics in cluster structures. 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 disappearing 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.

Original languageEnglish
Peer-reviewed scientific journalNeurocomputing
Volume121
Issue number9
Pages (from-to)317-327
Number of pages11
ISSN0925-2312
DOIs
Publication statusPublished - 09.12.2013
MoE publication typeA1 Journal article - refereed

Keywords

  • 511 Economics
  • Cluster analysis
  • Self-Organizing Time Map
  • Visual dynamic clustering

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