Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. Indexes for estimating the spatial consistency and classification quality are proposed in addition. The methods were originally proposed in the field of brain imagerie (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).
|Author||Jeremy Gelb [aut, cre], Philippe Apparicio [ctb]|
|Maintainer||Jeremy Gelb <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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