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)naspaclust_0.2.2.zip(r-4.6)naspaclust_0.2.2.zip(r-4.5)
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
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 787 downloads 9 exports 8 dependencies

Last updated from:4c81348c83. Checks:4 NOTE, 2 OK, 3 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE127
source / vignettesOK243
linux-release-x86_64NOTE149
macos-release-arm64FAIL53
macos-oldrel-arm64FAIL37
windows-develNOTE97
windows-releaseFAIL28
windows-oldrelNOTE93
wasm-releaseOK107

Exports:abcfgwcfgwcfgwcuvfpafgwcgsafgwchhofgwcifafgwcpsofgwctlbofgwc

Dependencies:audiobeeprrbibutilsRcppRcppArmadillordistRdpackstabledist