Package: naspaclust 0.2.2

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.

Authors:Bahrul Ilmi Nasution [aut, cre], Robert Kurniawan [aut], Rezzy Eko Caraka [aut]

naspaclust_0.2.2.tar.gz
naspaclust_0.2.2.zip(r-4.7-any)naspaclust_0.2.2.zip(r-4.6-any)naspaclust_0.2.2.zip(r-4.5-any)
naspaclust_0.2.2.tgz(r-4.6-any)naspaclust_0.2.2.tgz(r-4.5-any)
naspaclust_0.2.2.tar.gz(r-4.7-any)naspaclust_0.2.2.tar.gz(r-4.6-any)
naspaclust_0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
naspaclust/json (API)

# Install 'naspaclust' in R:
install.packages('naspaclust', repos = c('https://bmlmcmc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bmlmcmc/naspaclust/issues

Datasets:

On CRAN:

Conda:

2.00 score 451 downloads 9 exports 8 dependencies

Last updated from:4c81348c83. Checks:7 NOTE, 2 OK. Indexed: yes.
A new build is currently in progress.

TargetResultTimeFilesSyslog
linux-develNOTE136
source / vignettesOK217
linux-releaseNOTE127
macos-releaseNOTE181
macos-oldrelNOTE197
windows-develNOTE76
windows-releaseNOTE82
windows-oldrelNOTE85
wasm-releaseOK105

Exports:abcfgwcfgwcfgwcuvfpafgwcgsafgwchhofgwcifafgwcpsofgwctlbofgwc

Dependencies:audiobeeprrbibutilsRcppRcppArmadillordistRdpackstabledist