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Clustering in Tableau for Beginers .pptx
- 2. A method of combining data into similar groups based on provided variables *Tableau uses color to distinguish between clusters Clustering Analysis
- 3. Recommendations Location Planning Customer Loyalty Programs Climate Change Use Cases
- 6. Step 4: Analyze the Clusters
- 7. Tableau uses the k-means algorithm for clustering. For a given number of clusters k, the algorithm partitions the data into k clusters. Each cluster has a center (centroid) that is the mean value of all the points in that cluster. Clustering
- 11. 𝑠𝑠𝑏 𝑠𝑠𝑤 (𝑁 − 𝑘) (𝑘 − 1) x Calinski-Harabasz criterion
- 13. Variables: Sum of CO2 emissions per capita (metric tons) Sum of CO2 emissions, total (KtCO2) Level of Detail: Country name Scaling: Normalized Inputs for Clustering
- 14. Number of Clusters: 5 Number of Points: 218 Between-group Sum of Squares: 5.0161 Within-group Sum of Squares: 0.64986 Total Sum of Squares: 5.6659 Summary Diagnostics
Editor's Notes
- K-means locates centers through an iterative procedure that minimizes distances between individual points in a cluster and the cluster center. In Tableau, you can specify a desired number of clusters, or have Tableau test different values of k and suggest an optimal number of clusters (see Determining the optimal number of clusters).
- Tableau uses the Calinski-Harabasz criterion to assess cluster quality. The Calinski-Harabasz criterion is defined as where SSB is the overall between-cluster variance, SSW the overall within-cluster variance, k the number of clusters, and N the number of observations. The greater the value of this ratio, the more cohesive the clusters (low within-cluster variance) and the more distinct/separate the individual clusters (high between-cluster variance).