Abstract
Clustering is a classical tool in image analysis, with wide applications. Yet, most of its algorithmic solutions include a considerable amount of stochasticity, e.g. due to different initialisations. Here, we introduce a clustering method rooted on self organizing maps, that exploits the maps’ intrinsic variability, to produce reliable clustering. Although only a subset of the data is consistently clustered, we show that this set is trustworthy, and can be used for posterior classification.
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Gonçalves, N., Vigário, R. (2012). Clustering through SOM Consistency. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31295-3_8
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DOI: https://doi.org/10.1007/978-3-642-31295-3_8
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