The Novel Improved Hybrid Clustering Algorithm of Particle Swarm and K-Means Considering Applications

The novel improved hybrid clustering algorithm of the particle swarm and K-Means considering applications is well studied in this paper. This research work considers each object present in the space as a particle with a certain mass, and there is a spherical symmetrical virtual data field around it. The output result of the system in this paper is closer to the true target value. The reason is that the analysis system set in this paper applies the clustering objective function, which makes up for the shortcomings of traditional systems that are easy to fall into local extreme values, slow clustering speed and poor analysis accuracy. The K-means is optimized with the particle swarm considering the data structure. The proposed method is simulated on different database.