@article{Vesanto:2000,
title = {Clustering of the self-organizing map},
author = {J. Vesanto and E. Alhoniemi},
booktitle = {Neural Networks, IEEE Transactions on},
pages = {586-600},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=846731},
volume = {11},
year = {2000},
description = {Welcome to IEEE Xplore 2.0: Clustering of the self-organizing map},
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},
issn = {1045-9227}, doi = {10.1109/72.846731},
keywords = {imported proj:bk }
}
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