Abstract
The Growing Self Organising Map (GSOM) has been presented as an extended version of the Self Organising Map (SOM) which has significant advantages for knowledge discovery applications. In this article, we present a further extension to the GSOM in which the cluster identification process can be automated. The self-generating ability of the GSOM is used to identify the paths along which the GSOM grew, and these paths are used to develop a skeleton of the data set. Such a skeleton is then used as a base for separating the clusters in the data
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© 2001 Springer-Verlag Berlin Heidelberg
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Alahakoon, D., Halgamuge, S.K., Srinivasan, B. (2001). Mining a Growing Feature Map by Data Skeleton Modelling. In: Kandel, A., Last, M., Bunke, H. (eds) Data Mining and Computational Intelligence. Studies in Fuzziness and Soft Computing, vol 68. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1825-3_9
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DOI: https://doi.org/10.1007/978-3-7908-1825-3_9
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2484-1
Online ISBN: 978-3-7908-1825-3
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