This document serves as an introduction to clustering and details the clustering algorithms offered by Datagran, emphasizing the importance of unsupervised learning in discovering hidden patterns in unlabeled data. Clustering, considered a vital unsupervised learning problem, involves grouping similar instances into clusters and finds applications in customer segmentation, semi-supervised learning, and anomaly detection. The text outlines various clustering algorithms such as k-means, bisecting k-means, hierarchical clustering, and Gaussian Mixture Models, each with its unique approach to grouping data. It also describes the process flow for clustering, including data preparation, model testing, and prediction, and highlights the significance of hyperparameters that can be adjusted to enhance model performance.