Abstract
In this paper we introduce Self-Organizing Map-based techniques that can reveal structural cluster changes in two related data sets from different time periods in a way that can explain the new result in relation to the previous one. These techniques are demonstrated using a real-world data set from the World Development Indicators database maintained by the World Bank. The results verify that the methods are capable of revealing changes in cluster strucure and membership, corresponding to known changes in economic fortunes of countries. © Springer-Verlag Berlin Heidelberg 2005.
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CITATION STYLE
Denny, & Squire, D. M. G. (2005). Visualization of cluster changes by comparing self-organizing maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3518 LNAI, pp. 410–419). Springer Verlag. https://doi.org/10.1007/11430919_48
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