Learning Automata Clustering

Abstract Clustering of data points has been a profound research avenue in the history of machine learning algorithms. Using learning automata which are autonomous decision making entities, in this paper, the learning automata clustering algorithm is proposed. In learning automata clustering, each data point is affiliated with a learning automaton where the learning automaton determines the cluster membership of that data point. The cluster rectification is done through a reinforcement signal for each learning automaton which is fabricated from the Euclidean distance of that data point and the mean value of its designated cluster. Finally, the learning automata clustering is compared with four centroid-based clustering algorithms, K-means, K-means++, K-medians, and K-medoids and results demonstrate the high clustering accuracy and comparable Silhouette coefficient of the proposed method.