Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN

Clustering, an unsupervised machine learning technique, plays a crucial role in partitioning unlabeled data into meaningful groups. K-means, known for its simplicity, has gained popularity as clustering method. However, both K-means and the LAC algorithm, which utilize automata, are sensitive to selection of initial points. To overcome this limitation, we propose enhanced algorithm based on K-Harmonic means approach. We evaluate performance seven datasets demonstrate superiority over other representative algorithms. Moreover, tailor address controller placement problem software-defined networks, critical field context. optimize relevant parameters such switch–controller delay, intercontroller load balancing, leverage automata. In our comparative analysis conducted Python, benchmark against spectral, algorithms four different network topologies. The results unequivocally show that proposed outperforms others, achieving significant improvement ranging from 3 11 percent. This research contributes advancement techniques their practical application networks.