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 4 scripts 559 downloads 9 exports 8 dependencies

Last updated from:4c81348c83. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE116
source / vignettesOK166
linux-release-x86_64NOTE113
macos-release-arm64NOTE190
macos-oldrel-arm64NOTE201
windows-develNOTE87
windows-releaseNOTE77
windows-oldrelNOTE80
wasm-releaseOK102

Exports:abcfgwcfgwcfgwcuvfpafgwcgsafgwchhofgwcifafgwcpsofgwctlbofgwc

Dependencies:audiobeeprrbibutilsRcppRcppArmadillordistRdpackstabledist