# Scalable Co-Clustering for Large-Scale Data through Dynamic Partitioning and Hierarchical Merging

arXiv: https://arxiv.org/abs/2410.18113 · HTML: https://mlsanity.com/paper/2410.18113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 9 Oct 2024.

## Abstract (arXiv)

Co-clustering simultaneously clusters rows and columns, revealing more fine-grained groups. However, existing co-clustering methods suffer from poor scalability and cannot handle large-scale data. This paper presents a novel and scalable co-clustering method designed to uncover intricate patterns in high-dimensional, large-scale datasets. Specifically, we first propose a large matrix partitioning algorithm that partitions a large matrix into smaller submatrices, enabling parallel co-clustering. This method employs a probabilistic model to optimize the configuration of submatrices, balancing the computational efficiency and depth of analysis. Additionally, we propose a hierarchical co-cluster merging algorithm that efficiently identifies and merges co-clusters from these submatrices, enhancing the robustness and reliability of the process. Extensive evaluations validate the effectiveness and efficiency of our method. Experimental results demonstrate a significant reduction in computation time, with an approximate 83% decrease for dense matrices and up to 30% for sparse matrices.