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Benchmarking Deep Clustering Algorithms With ClustPy

MCML Authors

Abstract

Deep clustering algorithms have gained popularity as they are able to cluster complex large-scale data, like images. Yet these powerful algorithms require many decisions w.r.t. architecture, learning rate and other hyperparameters, making it difficult to compare different methods. A comprehensive empirical evaluation of novel clustering methods, however, plays an important role in both scientific and practical applications, as it reveals their individual strengths and weaknesses. Therefore, we introduce ClustPy, a unified framework for benchmarking deep clustering algorithms, and perform a comparison of several fundamental deep clustering methods and some recently introduced ones. We compare these methods on multiple well known image data sets using different evaluation metrics, perform a sensitivity analysis w.r.t. important hyperparameters and perform ablation studies, e.g., for different autoencoder architectures and image augmentation. To our knowledge this is the first in depth benchmarking of deep clustering algorithms in a unified setting.

inproceedings

Workshop @ICDM 2023

Workshop at the 23rd IEEE International Conference on Data Mining. Shanghai, China, Dec 01-04, 2023.

Authors

C. Leiber • L. Miklautz • C. Plant • C. Böhm

Links

DOI GitHub

Research Area

 A3 | Computational Models

BibTeXKey: LMP+23a

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