  Deep clustering (DC), a fusion of deep representation learning and
clustering, has recently demonstrated positive results in data science,
particularly text processing and computer vision. However, joint optimization
of feature learning and data distribution in the multi-dimensional space is
domain-specific, so existing DC methods struggle to generalize to other
application domains (such as data integration and cleaning). In data management
tasks, where high-density embeddings and overlapping clusters dominate, a data
management-specific DC algorithm should be able to interact better with the
data properties for supporting data cleaning and integration tasks. This paper
presents a deep clustering algorithm for tabular data (TableDC) that reflects
the properties of data management applications, particularly schema inference,
entity resolution, and domain discovery. To address overlapping clusters,
TableDC integrates Mahalanobis distance, which considers variance and
correlation within the data, offering a similarity method suitable for tables,
rows, or columns in high-dimensional latent spaces. TableDC provides
flexibility for the final clustering assignment and shows higher tolerance to
outliers through its heavy-tailed Cauchy distribution as the similarity kernel.
The proposed similarity measure is particularly beneficial where the embeddings
of raw data are densely packed and exhibit high degrees of overlap. Data
cleaning tasks may involve a large number of clusters, which affects the
scalability of existing DC methods. TableDC's self-supervised module
efficiently learns data embeddings with a large number of clusters compared to
existing benchmarks, which scale in quadratic time. We evaluated TableDC with
several existing DC, Standard Clustering (SC), and state-of-the-art bespoke
methods over benchmark datasets. TableDC consistently outperforms existing DC,
SC, and bespoke methods.
