提出动态分块与分层合并方法，实现大规模数据的高效共聚类。
Scalable Co-Clustering for Large-Scale Data through Dynamic Partitioning and Hierarchical Merging
- 将大矩阵动态分块，支持并行共聚类计算。
- 密集矩阵计算时间减少约83%，稀疏矩阵减少最多30%。
- 适合处理高维大规模数据，提升分析效率与结果可靠性。
共聚类能同时对行和列进行聚类，揭示更细粒度的分组模式。然而，现有共聚类方法在大规模数据上扩展性差。本文提出一种新颖且可扩展的共聚类方法，用于发现高维大规模数据中的复杂模式。首先设计了一种大矩阵分块算法，将大矩阵划分为较小子矩阵，实现并行共聚类；该算法采用概率模型优化子矩阵配置，在计算效率与分析深度间取得平衡。此外，提出分层共聚类合并算法，高效识别并合并子矩阵中的共聚类，增强过程的鲁棒性与可靠性。大量实验验证了方法的有效性与高效性：实验结果表明，密集矩阵计算时间降低约83%，稀疏矩阵降低最多达30%。
原文摘要 · Abstract (English)
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.
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