Self-organizing maps
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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.