Example for Cluster K-Means

A business analyst wants to classify 22 successful small-to-medium size manufacturing companies into meaningful groups for future analyses. The analyst collects data on the number of clients, rate of return, sales, and the years the companies have been in business. To start the partition process, the analyst divides the companies into three initial groups: established, mid-growth, and young.

  1. Open the sample data set, BusinessMetrics.MTW.
  2. Choose Stat > Multivariate > Cluster K-Means.
  3. In Variables, enter Clients 'Rate of Return' Sales Years.
  4. Under Specify partition by, select Initial partition column and enter Initial.
  5. Select Standardize variables.
  6. Click Storage. In Cluster membership column, type Final.
  7. Click OK in each dialog box.

Interpret the results

Based on the initial grouping provided by the business analyst, cluster k-means classifies the 22 companies into 3 clusters: 4 established companies, 8 mid-growth companies, and 10 young companies. Minitab stores the cluster membership for each observation in the Final column in the worksheet.

Cluster 1 (established companies) has the least variability of the 3 clusters, with the smallest value for the average distance from centroid (0.578). Cluster 1 also has the fewest observations (4).

K-means Cluster Analysis: Clients, Rate of Return, Sales, Years

Method Number of clusters 3 Standardized variables Yes
Final Partition Within Average Maximum cluster distance distance Number of sum of from from observations squares centroid centroid Cluster1 4 1.593 0.578 0.884 Cluster2 8 8.736 0.964 1.656 Cluster3 10 12.921 1.093 1.463
Cluster Centroids Grand Variable Cluster1 Cluster2 Cluster3 centroid Clients 1.2318 0.5225 -0.9108 0.0000 Rate of Return 1.2942 0.2217 -0.6950 0.0000 Sales 1.1866 0.5157 -0.8872 0.0000 Years 1.2030 0.5479 -0.9195 0.0000
Distances Between Cluster Centroids Cluster1 Cluster2 Cluster3 Cluster1 0.0000 1.5915 4.1658 Cluster2 1.5915 0.0000 2.6488 Cluster3 4.1658 2.6488 0.0000
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