提出高效非光滑优化算法，解决大规模数据聚类难题
Clust-Splitter - an Efficient Nonsmooth Optimization-Based Algorithm for Clustering Large Datasets
- 基于非光滑优化构建三步求解框架，提升初始点质量
- 在真实大规模数据集上实现高精度聚类，性能媲美顶尖方法
- 适合处理高维海量数据，尤其适用于资源受限场景
聚类是数据挖掘和机器学习中的基础任务，尤其适用于分析大规模数据。本文提出Clust-Splitter，一种基于非光滑优化的高效算法，用于解决大规模数据集中的最小平方和聚类问题。该方法通过一系列三个非光滑优化问题来实现：两个辅助问题用于生成合适的初始点，随后是主聚类模型。为有效求解这些问题，将有限内存束方法与增量策略结合，开发出Clust-Splitter算法。我们在具有大量属性和数据点的真实世界数据集上评估该算法，并与多种先进大规模聚类算法进行比较。实验结果表明，该方法在处理超大规模数据集时具有高效性，且聚类结果质量与现有最优方法相当。
原文摘要 · Abstract (English)
Clustering is a fundamental task in data mining and machine learning, particularly for analyzing large-scale data. In this paper, we introduce Clust-Splitter, an efficient algorithm based on nonsmooth optimization, designed to solve the minimum sum-of-squares clustering problem in very large datasets. The clustering task is approached through a sequence of three nonsmooth optimization problems: two auxiliary problems used to generate suitable starting points, followed by a main clustering formulation. To solve these problems effectively, the limited memory bundle method is combined with an incremental approach to develop the Clust-Splitter algorithm. We evaluate Clust-Splitter on real-world datasets characterized by both a large number of attributes and a large number of data points and compare its performance with several state-of-the-art large-scale clustering algorithms. Experimental results demonstrate the efficiency of the proposed method for clustering very large datasets, as well as the high quality of its solutions, which are on par with those of the best existing methods.
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