Clustering of the self-organizing map

@article{citeulike:2210011, abstract = {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}, author = {Vesanto, J. and Alhoniemi, E. }, booktitle = {Neural Networks, IEEE Transactions on}, citeulike-article-id = {2210011}, doi = {10.1109/72.846731}, journal = {Neural Networks, IEEE Transactions on}, keywords = {machine\_learning, som, visualization}, number = {3}, pages = {586--600}, posted-at = {2008-01-09 06:38:28}, priority = {3}, title = {Clustering of the self-organizing map}, url = {http://dx.doi.org/10.1109/72.846731}, volume = {11}, year = {2000} }

TY - JOUR ID - citeulike:2210011 L3 - citeulike-article-id:2210011 TI - Clustering of the self-organizing map T2 - Neural Networks, IEEE Transactions on JF - Neural Networks, IEEE Transactions on VL - 11 IS - 3 SP - 586 EP - 600 N2 - 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 KW - machine_learning KW - som KW - visualization AU - Vesanto, J AU - Alhoniemi, E PY - 2000/// UR - http://dx.doi.org/10.1109/72.846731 ER -