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naspaclust
View on CRAN: Click
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Download and install naspaclust package within the R console
Install from CRAN:
install.packages("naspaclust")
Install from Github:
library("remotes")
install_github("cran/naspaclust")
Install by package version:
library("remotes")
install_version("naspaclust", "0.2.1")
Attach the package and use:
library("naspaclust")
Maintained by
Bahrul Ilmi Nasution
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-03-12
Latest Update: 2021-07-07
Description:
Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) 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) using Artificial Bee Colony algorithm.
How to cite:
Bahrul Ilmi Nasution (2021). naspaclust: Nature-Inspired Spatial Clustering. R package version 0.2.1, https://cran.r-project.org/web/packages/naspaclust. Accessed 03 Feb. 2025.
Previous versions and publish date:
0.1.0 (2021-03-12 10:30)
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Other R packages that naspaclust depends,
imports, suggests or enhances
Complete documentation for naspaclust
Functions, R codes and Examples using
the naspaclust R package
Some associated functions: abcfgwc . census2010 . census2010dist . census2010pop . fgwc . fgwcuv . fpafgwc . gsafgwc . hhofgwc . ifafgwc . psofgwc . tlbofgwc .
Some associated R codes: abcfgwc.R . data.R . ei.R . fgwc.R . fpafgwc.R . gsafgwc.R . hhofgwc.R . ifafgwc.R . index.R . mainfunction.R . naspaclust.R . psofgwc.R . tlbofgwc.R . Full naspaclust package functions and examples
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