naspaclust: Nature-Inspired Spatial Clustering

Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.

Version:	0.2.1
Depends:	R (≥ 3.5.0)
Imports:	Rdpack, rdist, stabledist, beepr
Suggests:	ppclust, spatialClust, cluster, ggplot2
Published:	2021-07-07
DOI:	10.32614/CRAN.package.naspaclust
Author:	Bahrul Ilmi Nasution [aut, cre], Robert Kurniawan [aut], Rezzy Eko Caraka [aut]
Maintainer:	Bahrul Ilmi Nasution <bahrulnst at gmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	naspaclust results

Documentation:

Reference manual:

naspaclust.pdf

Downloads:

Package source:	naspaclust_0.2.1.tar.gz
Windows binaries:	r-devel: naspaclust_0.2.1.zip, r-release: naspaclust_0.2.1.zip, r-oldrel: naspaclust_0.2.1.zip
macOS binaries:	r-release (arm64): naspaclust_0.2.1.tgz, r-oldrel (arm64): naspaclust_0.2.1.tgz, r-release (x86_64): naspaclust_0.2.1.tgz, r-oldrel (x86_64): naspaclust_0.2.1.tgz
Old sources:	naspaclust archive

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