Hierarchical Clusters Example

Hierarchical Clusters Example

Problem Statement

We have collected data on several different models of cars with respect to several parameters. Create a dendogram that displays the hierarchical relationship between the vehicles.

How to perform analysis

Step 1: Open Sigma Magic
  1. Click on the Sigma Magic button on the Excel toolbar.
  2. Click on the New button to create a new project.
Step 2: Add the analysis template
  1. Click on the Tool Wizard to add the analysis template.
  2. Click on Analytics and then Hierarchical Clusters.


Step 3: Specify analysis options
A new worksheet will be added to your workbook. Analysis Setup will be automatically opened, in the setup tab specify the survey results.


Click on Data to specify the data required for this analysis.



Click the Verify tab to ensure all the inputs are okay and shown in a green checkmark.



Step 4: Generate analysis result
Click OK and then click Compute Outputs to get the final results.


Interpretation of Results

1.  Clustering Method & Distance Metric:

  • The algorithm used is Ward’s method (ward.D2), which minimizes variance within clusters for better cohesion.
  • Euclidean distance is used to measure the similarity between data points.

2.  Number of Clusters Identified:

  • The analysis resulted in 3 clusters, as indicated in the Model Output section and highlighted in the dendrogram with red boxes.

3.  Dataset Details & Standardization:

  • The data type is continuous, and 10 variables were analyzed.
  • The analysis was performed on 32 objects (data points).
  • Standardization was not applied, meaning variables retained their original scales.

4.  Dendrogram Interpretation:

  • The dendrogram visually represents the hierarchical clustering process.
  • The three red boxes show the final clusters formed by cutting the tree at an appropriate height.

5.  Cluster Assignment in Model Output:

  • The rightmost Model Output column assigns a cluster label (1, 2, or 3) to each object.
  • This helps in further analysis, such as understanding patterns within each cluster.

6.  Conclusion:

  • A hierarchical tree structure was successfully created.
  • The clustering helps identify natural groupings in the data, which can be used for insights like market segmentation, pattern recognition, or anomaly detection.

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