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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.
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).
| Number of clusters | 3 |
|---|---|
| Standardized variables | Yes |
| Number of observations | Within cluster sum of squares | Average distance from centroid | Maximum distance from 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 |
| Variable | Cluster1 | Cluster2 | Cluster3 | Grand 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 |
| 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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