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　　The self-organizing map (SOM) network model is designed to solve problems that involve tasks such as clustering, visualization, and abstraction. When the number of SOM units is large, similar units of the map need to be grouped in order to facilitate analysis of the self-organizing map and the data. This thesis extends the original Kohonen SOM network model by including a contiguity-constrained clustering method based on a nonlinear metric to perform clustering of the SOM units generated by the Kohonen SOM network model. This nonlinear metric is proven to be more robust than the Euclidean norm in the context of c-means clustering and fuzzy c-means clustering. This thesis is to explore the performance of this two-level clustering algorithm when it is in the presence and in the absence of the nonlinear metric, and make a comparison between this two-level clustering method and other clustering algorithms by using real-world data in three different domains.

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