Adding anomalies to the Dataset
Without labels for which data points are anomalies, you have no way to know how many anomalies you should get.

The closest thing to adding anomalies to the dataset is using synthetic data for anomaly detection. I have seen some practitioners doing this, and failing. When you create the synthetic data, it matches your inductive bias*. The real world very rarely matches that inductive bias*. Afterwards, you can easily find a clustering algorithm with the same inductive bias* which can flag your synthetic data as anomalies. However, when that algorithm is deployed it will fail miserably.

*in plain language, inductive bias is the assumptions you have of how the world should be.