Pivot analysis of the outliers

We can apply the business intelligence pivot tables to explore the ranges of the groups for every variable of the dataset. Using this method, we can visualize the groups that appear to be outliers.

Kaggle credit card fraud dataset

With the information of the group assignment with K-means clustering, we can explore the outliers for each dataset. From the amount chart of credit card transactions in Figure 7.9, we see that groups three and four have compact and similar values with a combined range between 355 and 1402:

Figure 7.9 – Credit card amount field groups

From Figure 7.9, we could conclude that the possible outliers are as follows:

• Group 1 (ranges between 0 and 86)
• Group 5 (ranges between 89 and 322)
• Group 4 (has just one record with a big value of 3828, which indicates an anomaly)

Combining the analysis with the V1 field groups, in Figure 7.10, we can examine whether we can confirm the...