Statistically Rigorous Testing of Clustering Implementations

Clustering is a widely-used and well-studied AI branch, but defining clustering correctness, as well as verifying and validating clustering implementations, remains a challenge. To address this, we propose a statistically rigorous approach that couples differential clustering with statistical hypothesis testing, namely we conduct statistical hypothesis testing on the outcome (distribution) of differential clustering to reveal problematic outcomes. We employed this approach on widely-used clustering algorithms implemented in popular ML toolkits; the toolkits were tasked with clustering datasets from the Penn Machine Learning Benchmark. The results indicate that there are statistically significant differences in clustering outcomes in a variety of scenarios where users might not expect clustering outcome variation.