Abstract
An evolving clustering algorithm applying the adaptive-distance measure is developed. An incorporated fuzzy decision support procedure classifies the current income. The decision support increases the algorithm robustness. As it discovers on-line clusters with different shape and orientation it is applicable to a wide range of practical tasks as diagnostics and prognostics, system identification, real time classification.
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Georgieva, O., Nedev, S. (2010). Decision Support for Evolving Clustering. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_38
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DOI: https://doi.org/10.1007/978-3-642-14746-3_38
Publisher Name: Springer, Berlin, Heidelberg
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