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Knowledge and Information Systems

, Volume 44, Issue 2, pp 493–505 | Cite as

Automated and weighted self-organizing time maps

  • Peter Sarlin
Regular Paper

Abstract

This paper proposes schemes for automated and weighted self-organizing time maps (SOTMs). The SOTM provides means for a visual approach to evolutionary clustering, which aims at producing a sequence of clustering solutions. This task we denote as visual dynamic clustering. The implication of an automated SOTM is not only a data-driven parametrization of the SOTM, but also the feature of adjusting the training to the characteristics of the data at each time step. The aim of the weighted SOTM is to improve learning from more trustworthy or important data with an instance-varying weight. The schemes for automated and weighted SOTMs are illustrated on two real-world datasets: (i) country-level risk indicators to measure the evolution of global imbalances and (ii) credit applicant data to measure the evolution of firm-level credit risks.

Keywords

Self-Organizing Time Map Weighting schemes Automation Quality measures 

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Center of Excellence SAFEGoethe University FrankfurtFrankfurt am MainGermany
  2. 2.RiskLab at Arcada University of Applied SciencesHelsinkiFinland

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