Multivariate Methods • Hierarchical Cluster
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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.
Use hierarchical clustering 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. For larger data tables, use K Means Cluster or Normal Mixtures.
Note: Hierarchical cluster supports character columns; K Means Cluster or Normal Mixtures require numeric columns.
Figure 10.1 Example of a Constellation Plot
Contents 
Overview of the Hierarchical Clustering Platform
Overview of Platforms for Clustering Observations
Example of Clustering
Launch the Hierarchical Cluster Platform
Clustering Method
Method for Distance Calculation
Data Structure
Transformations to Y, Columns Variables
Hierarchical Cluster Report
Dendrogram Report
Illustration of Dendrogram and Distance Graph
Clustering History Report
Hierarchical Cluster Options
Additional Examples of the Hierarchical Clustering Platform
Example of a Distance Matrix
Example of Wafer Defect Classification Using Spatial Measures
Statistical Details for the Hierarchical Clustering Platform
Spatial Measures
Distance Method Formulas

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Help created on 7/12/2018