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Cluster Analysis

Unsupervised learning techniques to find natural groupings and patterns in data

Cluster analysis, also called segmentation analysis or taxonomy analysis, partitions sample data into groups, or clusters. Clusters are formed such that objects in the same cluster are similar, and objects in different clusters are distinct. Statistics and Machine Learning Toolbox™ provides several clustering techniques and measures of similarity (also called distance metrics) to create the clusters. Additionally, cluster evaluation determines the optimal number of clusters for the data using different evaluation criteria. Cluster visualization options include dendrograms and silhouette plots. The toolbox also provides several anomaly detection features to identify outliers and novelties.

Cluster Analysis Basics

Click to go to the example, Cluster Gaussian Mixture Data Using Hard Clustering