Decision Support for Evolving Clustering

摘要

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.