This document describes an automated clustering and outlier detection program. The program normalizes data, performs principal component analysis to select important components, compares clustering algorithms, selects the best model using silhouette values, and produces outputs labeling clusters and outliers. It is demonstrated on a sample of 5,000 credit card customer records, identifying a small cluster of 3 accounts as outliers based on features like new status and high late payments.