Abstract
In this chapter, we concentrate on a granular data analysis, especially studying ways of information granulation. We show how information granules are constructed by a designer/user via a visual inspection of self-organizing maps (SOMs). SOMs are commonly used neural network architectures realizing a paradigm of unsupervised learning. The crux of the approach proposed here lies in the following
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a high level of interaction with user — it is worth stressing that the constructs (information granules) are delineated by a human on a basis of visualization of highly dimensional data,
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a solid support of the development of information granules cast in the framework of sets and fuzzy sets.
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Bargiela, A., Pedrycz, W. (2003). Self-Organizing Maps in the Design and Processing of Granular Information. In: Granular Computing. The Springer International Series in Engineering and Computer Science, vol 717. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1033-8_15
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DOI: https://doi.org/10.1007/978-1-4615-1033-8_15
Publisher Name: Springer, Boston, MA
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