Cluster formation is a research area focused on the organization and grouping of entities or data points into distinct clusters based on their similarities or relationships. This clustering process aims to identify patterns, trends, and relationships within a dataset, which can be useful for various applications such as data mining, machine learning, and network analysis. Cluster formation algorithms and techniques are used to categorize and group data points into clusters based on specific criteria, such as distance metrics, density, or similarity measures. The goal of cluster formation is to uncover hidden structures within datasets and facilitate analysis and decision-making processes.