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ESOM visualizations for quality assessment in clustering

 
: Ultsch, A.; Behnisch, M.; Lötsch, J.

:

Merényi, E.:
Advances in Self-Organizing Maps and Learning Vector Quantization : Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016
Cham: Springer International Publishing, 2016 (Advances in Intelligent Systems and Computing 428)
ISBN: 978-3-319-28517-7 (Print)
ISBN: 978-3-319-28518-4 (Online)
pp.39-48
Workshop on Self-Organizing Maps (WSOM) <11, 2016, Houston/Tex.>
English
Conference Paper
Fraunhofer IME ()

Abstract
Classical clustering algorithms as well as intrinsic evaluation criteria impose predefined structures onto a data set. If the structures do not fit the data, the clustering will fail and the evaluation criteria will lead to erroneous conclusions. Recently, the abstract U-matrix has been defined for emergent self-organizing maps (ESOM). In this work the abstract forms of the P- and the U* are defined in analogy to the P- and the U*-matrix on ESOM. The abstract U*-matrix can be used for AU*-clustering of data by taking account of density and distance structures. For AU*- clustering the structures seen on the ESOM serve as a supervising quality measure. In this way it can be determined whether an AU*-clustering represents important structures inherent to the high dimensional data. Importantly, AU*-clustering does not impose a geometric cluster shape, which may not fit the underlying data structure, onto the data set.

: http://publica.fraunhofer.de/documents/N-422487.html