Optimal adaptive k-means algorithm with dynamic adjustment of learning rate

Adaptive k-means clustering algorithms have been used in several artificial neural network architectures, such as radial basis function networks or feature-map classifiers, for a competitive partitioning of the input domain. The authors present a modification of the traditional k-means algorithm. This approach approximates an optimal clustering solution with an adaptive learning rate, which renders it usable even in situations where the statistics of the problem task slowly vary with time. Simulations comparing this improved adaptive k-means algorithm with other k-means variants are presented.<
>