Abstract
Within this paper a new data clustering algorithm is proposed based on classical clustering algorithms. Here k-means neurons are used as substitute for the original data points. These neurons are online adaptable extending the standard k-means clustering algorithm. They are equipped with perceptive fields to identify if a presented data pattern fits within its area it is responsible for.
In order to find clusters within the input data an extension of the ε-nearest neighbouring algorithm is used to find connected groups within the set of k-means neurons.
Most of the information the clustering algorithm needs are taken directly from the input data. Thus only a small number of parameters have to be adjusted.
The clustering abilities of the presented algorithm are shown using data sets from two different kind of applications.
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Schatten, R., Goerke, N., Eckmiller, R. (2005). Regional and Online Learnable Fields. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_9
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DOI: https://doi.org/10.1007/11552499_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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