Center of Gravity PSO for Partitioning Clustering
This work addresses clustering challenges for data analysis applications, but it is incremental as it modifies an existing PSO approach.
The paper tackled the problem of partition-based clustering by proposing a local best model of Particle Swarm Optimization (PSO) to overcome drawbacks of the global best model, resulting in performance measured using adjusted rand index and compared with k-means and global best PSO.
This paper presents the local best model of PSO for partition-based clustering. The proposed model gets rid off the drawbacks of gbest PSO for clustering. The model uses a pre-specified number of clusters K. The LPOSC has K neighborhoods. Each neighborhood represents one of the clusters. The goal of the particles in each neighborhood is optimizing the position of the centroid of the cluster. The performance of the proposed algorithms is measured using adjusted rand index. The results is compared with k-means and global best model of PSO.