Clustering of the self-organizing map

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 quant...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 11; no. 3; pp. 586 - 600
Main Authors Vesanto, J., Alhoniemi, E.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.05.2000
Subjects
Agglomeration
Clustering
Clustering methods
Computation
Data acquisition
Data analysis
Data mining
Data preprocessing
Data visualization
Electronic mail
Neural networks
Prototypes
Quantitative analysis
Topology
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Summary: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.
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ISSN:1045-9227
1941-0093
DOI:10.1109/72.846731