Approximation Algorithm for the Balanced 2-Correlation Clustering Problem

The Correlation Clustering Problem (CorCP) is a significant clustering problem based on the similarity of data. It has applications in different fields, such as machine learning, biology, and data mining, many problems other areas. In this paper, Balanced 2-CorCP (B2-CorCP) introduced examined, new interesting variant CorCP described. goal to partition vertex set into two clusters with equal size, that number disagreements minimized. We first present polynomial time algorithm for B2-CorCP $M$ -positive edge dominant graphs ( xmlns:xlink="http://www.w3.org/1999/xlink">$M\geqslant 3$ ). Then, we provide series numerical experiments, results show effectiveness our algorithm.