The aim in clustering is grouping the data at hand based on its inherent characteristics. While performing this operation, labeled data is not used as in classification and regression. In order to fully explain the clustering concept, we will start with its definition. Then, we will introduce the k-meansk-means clustering and DBSCANDensity-based spatial clustering of applications with noise (DBSCAN) clustering algorithms. These algorithms have different working principles. Hence, they provide different clusters for the same data at hand. While handling each clustering algorithm, we will first cover its theoretical background. Then, we will explore its formation in Python on PC. Afterward, we will introduce methods to deploy the formed clustering algorithm to the nRF microcontroller. As end of the chapter applications, we will provide solution to two real-life problems via clustering as fall detection and image quantization.