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Multivariate Methods > Hierarchical Cluster
Publication date: 06/21/2023

Hierarchical Cluster

Group Observations Using a Tree of Clusters

Clustering is a multivariate technique that groups together observations that share similar values across a number of variables. Use it to understand the clumping structure of your data.

Hierarchical clustering combines clusters successively. The method begins by treating each observation as its own cluster. Then, at each step, the two clusters that are closest in terms of distance are combined into a single cluster. The result is depicted as a tree, called a dendrogram.

Typically, hierarchical clustering is useful for small data tables with no more than several tens of thousands of rows. The algorithm is time-intensive and can run slowly for larger data tables. However, the Hierarchical Cluster platform also provides two methods, Fast Ward and Hybrid Ward, that increase computation time and are useful for clustering larger data tables.

Note: Hierarchical cluster supports character columns; K Means Cluster or Normal Mixtures require numeric columns.

Figure 13.1 Example of a Constellation PlotĀ 

Example of a Constellation Plot

Contents

Overview of the Hierarchical Clustering Platform

Overview of Platforms for Clustering Observations

Example of Hierarchical Clustering

Launch the Hierarchical Cluster Platform

The Hierarchical Clustering Report

Dendrogram
Clustering History

Hierarchical Cluster Platform Options

Additional Examples of Hierarchical Clustering

Example of a Distance Matrix
Example of Wafer Defect Classification Using Spatial Measures

Statistical Details for the Hierarchical Cluster Platform

Statistical Details for Spatial Measures
Statistical Details for Distance Methods
Statistical Details for Near-Neighbor Joining Cycles
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