综述十年来自组织映射在聚类分析中的改进与应用进展
A Survey on Recent Advances in Self-Organizing Maps
- 梳理近十年 SOM 算法的核心改进与方法演进
- 总结 SOM 在多场景下适应性增强的技术路径
- 聚焦商业应用中的数据管理挑战与解决方案
自组织映射是一种在多种数据场景中用于聚类分析的强大工具。自科赫嫩开创性工作以来，已提出众多变体与改进。本文回顾过去十年的发展，系统梳理原始 SOM 算法的主要演进，以及为适应不同应用场景和用户需求所取得的方法论进展。特别关注一个关键且重要的应用领域——SOM 的商业化应用，涉及特定的数据管理问题。
原文摘要 · Abstract (English)
Self-organising maps are a powerful tool for cluster analysis in a wide range of data contexts. From the pioneer work of Kohonen, many variants and improvements have been proposed. This review focuses on the last decade, in order to provide an overview of the main evolution of the seminal SOM algorithm as well as of the methodological developments that have been achieved in order to better fit to various application contexts and users' requirements. We also highlight a specific and important application field that is related to commercial use of SOM, which involves specific data management.
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