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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

Choose Cluster Analysis Method

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

Categories

Hierarchical Clustering
Produce nested sets of clusters
k-Means and k-Medoids Clustering
Cluster by minimizing mean or medoid distance, and calculate Mahalanobis distance
Density-Based Spatial Clustering of Applications with Noise
Find clusters and outliers by using the DBSCAN algorithm
Spectral Clustering
Find clusters by using graph-based algorithm
Gaussian Mixture Models
Cluster based on Gaussian mixture models using the Expectation-Maximization algorithm
Nearest Neighbors
Find nearest neighbors using exhaustive search or Kd-tree search
Hidden Markov Models
Markov models for data generation
Anomaly Detection
Detect outliers and novelties
Cluster Visualization and Evaluation
Plot clusters of data and evaluate optimal number of clusters

Featured Examples

Cluster Analysis

Cluster Analysis

Examine similarities and dissimilarities of observations or objects using cluster analysis in Statistics and Machine Learning Toolbox™. Data often fall naturally into groups (or clusters) of observations, where the characteristics of objects in the same cluster are similar and the characteristics of objects in different clusters are dissimilar.