Vesanto J, Alhoniemi E
Neural Networks Research Centre, Helsinki University of Technology, Helsinki, Finland.
IEEE Trans Neural Netw. 2000;11(3):586-600. doi: 10.1109/72.846731.
The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using k-means are investigated. The two-stage procedure--first using SOM to produce the prototypes that are then clustered in the second stage--is found to perform well when compared with direct clustering of the data and to reduce the computation time.
自组织映射（SOM）是数据挖掘探索阶段的一种优秀工具。它将输入空间投影到低维规则网格的原型上，这些原型可有效用于可视化和探索数据的属性。当SOM单元数量很大时，为便于对映射图和数据进行定量分析，需要将相似的单元进行分组，即聚类。本文考虑了SOM聚类的不同方法。特别地，研究了使用层次凝聚聚类和使用k均值的划分聚类。与直接对数据进行聚类相比，发现先使用SOM生成原型然后在第二阶段进行聚类的两阶段过程表现良好，并且能减少计算时间。
