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cs.LG2025
Measuring the Validity of Clustering Validation Datasets
Hyeon Jeon, Michaël Aupetit, DongHwa Shin +3
Clustering techniques are often validated using benchmark datasets where class labels are used as ground-truth clusters. However, depending on the datasets, class labels may not al…
cs.LG2023★ 1 cited
ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings
Hyeon Jeon, Aeri Cho, Jinhwa Jang +5
Dimensionality reduction (DR) techniques inherently distort the original structure of input high-dimensional data, producing imperfect low-dimensional embeddings. Diverse distortio…
cs.LG2022★ 6 cited
Sanity Check for External Clustering Validation Benchmarks using Internal Validation Measures
Hyeon Jeon, Michael Aupetit, DongHwa Shin +3
We address the lack of reliability in benchmarking clustering techniques based on labeled datasets. A standard scheme in external clustering validation is to use class labels as gr…