Statistically rigorous testing of clustering implementations

Document Type

Conference Proceeding

Publication Date

5-17-2019

Abstract

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.

Identifier

85067125775 (Scopus)

ISBN

[9781728104928]

Publication Title

Proceedings 2019 IEEE International Conference on Artificial Intelligence Testing Aitest 2019

External Full Text Location

https://doi.org/10.1109/AITest.2019.000-1

First Page

91

Last Page

98

Grant

W911NF-13-2-0045

Fund Ref

National Science Foundation

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