Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning

arXiv:2301.00802v3 Announce Type: replace
Abstract: Deep learning methods are primarily proposed for supervised learning of images or text with limited applications to clustering problems. In contrast, tabular data with heterogeneous features pose unique challenges in representation learning, where deep learning has yet to replace traditional machine learning. This paper addresses these challenges in developing one of the first deep clustering methods for tabular data: Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS). G-CEALS is an unsupervised deep clustering framework for …

abstract applications arxiv challenges clustering contrast cs.ai cs.lg data deep learning distribution features images machine machine learning paper replace representation representation learning supervised learning tabular tabular data text traditional machine learning type unique