An improved opposition-based Crow Search Algorithm for Data Clustering

Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CSA) and Opposition-based Learning (OBL). The CSA is one of the meat-heuristic algorithms that is difficult at the exploration and exploitation stage, and thus, the clustering problem is susceptible to initialization for centrality of the clusters. In the proposed model, the crows change their position based on the OBL method. The position of the crows is updated using OBL to find the best position for the cluster. To evaluate the performance of the proposed model, the experiments were performed on 8 datasets from the UCI repository and compared with seven different clustering algorithms. The results show that the proposed model is more accurate, more efficient, and more robust than other clustering algorithms. Also, the convergence of the proposed model is better than other algorithms.