梳理聚类方法与应用，助你快速理解数据科学核心工具
Data clustering: a fundamental method in data science and management
- 对比传统与先进聚类方法，涵盖分组、层次、密度等策略
- 总结聚类在复杂数据处理中的实际效果与通用工作流程
- 适合初学者入门，也适合研究者把握前沿方向
本文探讨了数据聚类在数据科学中的关键作用，分析了包括划分型、层次型聚类在内的传统方法，以及适用于复杂结构数据集的流式聚类、基于密度的聚类、图聚类和基于模型的聚类等先进方法。文章阐明了聚类的核心原则，介绍了常用工具与框架，阐述了聚类在数据科学中的标准工作流程，讨论了实际应用中的挑战，并展示了其在多个领域的广泛应用。通过聚焦基础原理与应用场景，凸显聚类在推动创新与支持数据驱动决策方面的潜力。最后，文章展望了未来研究方向。
原文摘要 · Abstract (English)
This paper explores the critical role of data clustering in data science, emphasizing its methodologies, tools, and diverse applications. Traditional techniques, such as partitional and hierarchical clustering, are analyzed alongside advanced approaches such as data stream, density-based, graph-based, and model-based clustering for handling complex structured datasets. The paper highlights key principles underpinning clustering, outlines widely used tools and frameworks, introduces the workflow of clustering in data science, discusses challenges in practical implementation, and examines various applications of clustering. By focusing on these foundations and applications, the discussion underscores clustering's transformative potential. The paper concludes with insights into future research directions, emphasizing clustering's role in driving innovation and enabling data-driven decision-making.
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