A Data Clustering Method for Communication Network Based on Affinity Propagation

With the expansion of the network scale, the amount of data in the communication network is also increasing. Data mining technology can effectively analyze the data generated in the network. As one of the important technologies of data mining, clustering is also widely used in the field of communication. However, the general clustering algorithm has numerical oscillation or large computational complexity. This paper proposes an improved AP clustering algorithm and proposes the concepts of filtering data transfer objects and dynamic damping coefficients. Experiment results show that the algorithm proposed in this paper can effectively improve efficiency and adapt well to communication network data.