Figures index

From

Incorporating K-means, Hierarchical Clustering and PCA in Customer Segmentation

Azad Abdulhafedh

Journal of City and Development. 2021, 3(1), 12-30
  • Figure 1. frequency histograms of some main variables in the dataset
  • Figure 2. the correlation matrix of the variables in the dataset
  • Figure 3. dendrogram of the single linkage method
  • Figure 4. dendrogram of the complete linkage method
  • Figure 5. dendrogram of the average linkage method
  • Figure 6. cutting the dendrogram
  • Figure 7. Clusters resulted from fitting the Hierarchical clustering into the dataset
  • Figure 8. finding optimal number of clusters using the elbow method
  • Figure 9. Silhouette method for optimal number of clusters
  • Figure 10. Gap statistic method for optimal number of clusters
  • Figure 11. Clusters resulted by fitting K-means into the credit card dataset
  • Figure 12. correlation matrix of the variables after applying the PCA
  • Figure 13. the scree plot for finding the optimal number of PCs
  • Figure 14. the cumulative percent of variance explained by the PCs
  • Figure 15. PC2 against PC2
  • Figure 16. PC2 against PC3
  • Figure 17. PC3 against PC4
  • Figure 18. PC4 against PC5
  • Figure 19. contributions of variables to the 5 optimal PCs
  • Figure 20. clusters resulted from applying the PCA
  • Figure 21. clusters resulted from applying the PCA to the dataset
  • Figure 22. AVERAGE Silhouette width score for the updated K-means clustering
  • Figure 23. histograms of the four clusters or groups