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arXiv · 2210.09640

Clustering Categorical Data: Soft Rounding k-modes

Surya Teja Gavva·Karthik C. S.·Sharath Punna
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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.

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Surya Teja Gavva, Karthik C. S., Sharath Punna. 2022-10-18. Clustering Categorical Data: Soft Rounding k-modes. https://arxiv.org/abs/2210.09640

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