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Using the 18 selected variables for a sample of 1000 calls (787 legitimate, 213 fraudulent), WardĄ¯s Hierarchical Clustering algorithm with Euclidean distances under complete linkage was applied to further understand how the selected features may influence the classification of calls, thereby verifying that an appropriate combination of features are being considered in our model. This method builds a top-down tree-like hierarchy of clusters (data groups) by beginning with all the data points in one initial cluster and splitting clusters recursively until each data point has been assigned to a single cluster, and is particularly useful for large datasets. The algorithmĄ¯s effectiveness is evaluated by calculating its divisive coefficient, which measures the amount of clustering structure found. Coefficients close to 1 are preferable, indicating that the clusters formed describe the data well. In our case, a coefficient of 0.97 was obtained. |
IEEE/ICACT20220012 Slide.10
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