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. This paper presents a modification of the traditional k-means algorithm. In approximates an optimal clustering solution with an efficient adaptive learning rate, which renders it usable even in situations where the statistics of the problem task slowly varies with time. This modification is based on the optimality criterion for the k-means partition stating that all of the region in the optimal k-means partition have the same "within- cluster variation" when the number of regions in the partition is large and the underlying distribution for generating input patterns is smooth. The within-cluster variation of any cluster is defined as the expectation of the squared Euclidean distance between pattern vectors in that cluster and the center of that cluster. Simulations comparing this improved adaptive k-means algorithm with other k-means variants are presented.

ICSI Technical Report TR-91-017

Technical Report