Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning

TL;DR

This paper introduces a novel clustering method for categorical data that uses multi-granular competitive learning and encoding strategies to automatically discover nested cluster structures, demonstrating robustness and scalability.

Contribution

The paper proposes the MGCPL algorithm and CAME strategy, enabling effective, scalable clustering of categorical data with nested structures, outperforming existing methods.

Findings

MCDC achieves superior clustering accuracy on various datasets.

The method is robust to different categorical data distributions.

It has linear time complexity, suitable for large-scale data.

Abstract

Data set composed of categorical features is very common in big data analysis tasks. Since categorical features are usually with a limited number of qualitative possible values, the nested granular cluster effect is prevalent in the implicit discrete distance space of categorical data. That is, data objects frequently overlap in space or subspace to form small compact clusters, and similar small clusters often form larger clusters. However, the distance space cannot be well-defined like the Euclidean distance due to the qualitative categorical data values, which brings great challenges to the cluster analysis of categorical data. In view of this, we design a Multi-Granular Competitive Penalization Learning (MGCPL) algorithm to allow potential clusters to interactively tune themselves and converge in stages with different numbers of naturally compact clusters. To leverage MGCPL, we also propose a Cluster Aggregation strategy based on MGCPL Encoding (CAME) to first encode the data objects according to the learned multi-granular distributions, and then perform final clustering on the embeddings. It turns out that the proposed MGCPL-guided Categorical Data Clustering (MCDC) approach is competent in automatically exploring the nested distribution of multi-granular clusters and highly robust to categorical data sets from various domains. Benefiting from its linear time complexity, MCDC is scalable to large-scale data sets and promising in pre-partitioning data sets or compute nodes for boosting distributed computing. Extensive experiments with statistical evidence demonstrate its superiority compared to state-of-the-art counterparts on various real public data sets.