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Knowledge and Information Systems
Published in: Knowledge and Information Systems 2/2015

01-08-2015 | Regular Paper

Automated and weighted self-organizing time maps

Author: Peter Sarlin

Published in: Knowledge and Information Systems | Issue 2/2015

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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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Metadata
Title
Automated and weighted self-organizing time maps
Author
Peter Sarlin
Publication date
01-08-2015
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 2/2015
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-014-0762-y

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