Abstract
The Growing Self Organizing Map (GSOM), which is an extended version of the Self Organizing Map (SOM), has significant advantages when dealing with dynamically or incrementally changing data structures. The Data Skeleton Model (DSM) is a technique developed using the GSOM to identify clusters and relate them to the input data sequence. In this paper, we have developed the DSM as a visualization tool to automate the cluster identification process. We also demonstrate the advantage of using the Spread Factor (SF) in the GSOM for clustering data.
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