K-means competitive learning for non-stationary environments

A modified k-means competitive learning algorithm that can perform efficiently in situations where the input statistics are changing, such as in nonstationary environments, is presented. This modified algorithm is characterized by the membership indicator that attempts to balance the variations of all clusters and by the learning rate that is dynamically adjusted based on the estimated deviation of the current partition from an optimal one. Simulations comparing this new algorithm with other k-means competitive learning algorithms on stationary and nonstationary problems are presented