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Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features. Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields. We can use clustering analysis to gain some valuable insights from our data by seeing what groups the data points fall into when we apply a clustering algorithm. The 5 Clustering Algorithms Data Scientists Need to Know

K-Means Clustering


Mean-Shift Clustering


Density-Based Spatial Clustering of Applications with Noise (DBSCAN)


Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)


Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)