Clustering of the self-organizing map
Clustering of the self-organizing map
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DOI：
10.1109/72.846731
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发表时间：
2000-05-01
影响因子：
--
通讯作者：
Alhoniemi, E
中科院分区：
文献类型：
--
作者：
Vesanto, J;Alhoniemi, E
自组织映射（SOM）是数据挖掘探索阶段的一个很好的工具。它将输入空间投影到低维规则网格的原型上，可以有效地用于可视化和探索数据的属性。当SOM单元的数量较大时，为了便于对地图和数据进行定量分析，需要对相似的单元进行分组，即，集群。在本文中，不同的方法来聚类的SOM被认为是，特别是，使用层次凝聚聚类和部分聚类使用IC-均值进行了研究。的两个阶段的过程中，首先使用SOM产生的原型，然后在第二阶段的集群被发现表现良好，与直接聚类的数据相比，并减少计算时间。
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 Ic-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.