Clustering Categorical Data: Soft Rounding k-modes
Machine Learning
2023-10-10 v3 Data Structures and Algorithms
Abstract
Over the last three decades, researchers have intensively explored various clustering tools for categorical data analysis. Despite the proposal of various clustering algorithms, the classical k-modes algorithm remains a popular choice for unsupervised learning of categorical data. Surprisingly, our first insight is that in a natural generative block model, the k-modes algorithm performs poorly for a large range of parameters. We remedy this issue by proposing a soft rounding variant of the k-modes algorithm (SoftModes) and theoretically prove that our variant addresses the drawbacks of the k-modes algorithm in the generative model. Finally, we empirically verify that SoftModes performs well on both synthetic and real-world datasets.
Cite
@article{arxiv.2210.09640,
title = {Clustering Categorical Data: Soft Rounding k-modes},
author = {Surya Teja Gavva and Karthik C. S. and Sharath Punna},
journal= {arXiv preprint arXiv:2210.09640},
year = {2023}
}