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Automated and weighted self-organizing time maps

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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.

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Authors and Affiliations

  1. Center of Excellence SAFE, Goethe University Frankfurt, Grüneburgplatz 1, 60323 , Frankfurt am Main, Germany

    Peter Sarlin

  2. RiskLab at Arcada University of Applied Sciences, Helsinki, Finland

    Peter Sarlin

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  1. Peter Sarlin
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Correspondence to Peter Sarlin.

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Sarlin, P. Automated and weighted self-organizing time maps. Knowl Inf Syst 44, 493–505 (2015). https://doi.org/10.1007/s10115-014-0762-y

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  • DOI: https://doi.org/10.1007/s10115-014-0762-y

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