Data Clustering

Data Clustering refers to the process of segregating data into various groups or clusters. These organized subsets of data, or clusters, contain similar data points that exhibit common traits, attributes, or characteristics. Clustering is essentially a type of unsupervised machine learning where the data is unlabeled, and the algorithm identifies similarities to group them together. There are several methods used for data clustering including partitioning methods like K-means, hierarchical methods, density-based methods like DBSCAN, and grid-based methods. These methods differ based on how they form the clusters and the types of data they work best with. Data clustering aids in the organization of large amounts of data, making it easier for developers to handle, understand, and utilize in game development.