Regional and online learnable fields

Within This work 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 ∈-nearest neighboring algorithm is used to find connected groups within the set of k-means neurons. Most of the information the clustering algorithm needs is 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 kinds of applications.