Abstract
We present an approach to cluster large datasets that integrates the Kohonen Self Organizing Maps (SOM) with a dynamic clustering algorithm of symbolic data (SCLUST). A preliminary data reduction using SOM algorithm is performed. As a result, the individual measurements are replaced by micro-clusters. These micro-clusters are then grouped in a few clusters which are modeled by symbolic objects. By computing the extension of these symbolic objects, symbolic clustering algorithm allows discovering the natural classes. An application on a real data set shows the usefulness of this methodology.
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Lechevallier, Y., Verde, R., de Carvalho, F.d.A.T. (2006). Symbolic Clustering of Large Datasets. In: Batagelj, V., Bock, HH., Ferligoj, A., Žiberna, A. (eds) Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-34416-0_21
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DOI: https://doi.org/10.1007/3-540-34416-0_21
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