Are Deep Image Embedding Clustering Methods Effective for Heterogeneous Tabular Data?

Deep learning methods in the literature are invariably benchmarked on image data sets and then assumed to work on all data problems. Unfortunately, architectures designed for image learning are often not ready or optimal for non-image data without considering data-specific learning requirements. In this paper, we take a data-centric view to argue that deep image embedding clustering methods are not equally effective on heterogeneous tabular data sets. This paper performs one of the first studies on deep embedding clustering of seven tabular data sets using six state-of-the-art baseline methods proposed for image data sets. Our results reveal that the traditional clustering of tabular data ranks second out of eight methods and is superior to most deep embedding clustering baselines. Our observation is in line with the recent literature that traditional machine learning of tabular data is still a competitive approach against deep learning. Although surprising to many deep learning researchers, traditional clustering methods can be competitive baselines for tabular data, and outperforming these baselines remains a challenge for deep embedding clustering. Therefore, deep learning methods for image learning may not be fair or suitable baselines for tabular data without considering data-specific contrasts and learning requirements.