Density-based Clustering: Clusters are formed based on the density of the region — examples of this type: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and OPTICS (Ordering Points to Identify Clustering Structure). Hierarchal-based Clustering: Clusters are formed using a tree-type structure. Some clusters are predefined and then used to create new clusters — examples of this type: CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering, and using Hierarchies). Partitioning-based Clustering: Clusters are formed by partitioning the input data into k clusters — examples of this type: K-means , CLARANS (Clustering Large Applications based upon Randomized Search). Mock Data Generator
Basically all coding information share and other fun things will happened.