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naspaclust

Nature-Inspired Spatial Clustering

v0.2.2 · May 22, 2025 · GPL-3

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) <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.

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r-devel-macos-arm64 OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
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r-oldrel-windows-x86_64 OK
r-patched-linux-x86_64 OK
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dependencies in R code

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NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

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Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-devel-linux-x86_64-fedora-clang

dependencies in R code

Namespace in Imports field not imported from: ‘Rdpack’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespace in Imports field not imported from: ‘Rdpack’
  All declared Imports should be used.

Dependency Network

Dependencies Reverse dependencies Rdpack rdist stabledist beepr naspaclust

Version History

new 0.2.2 Mar 10, 2026
updated 0.2.2 ← 0.2.1 diff May 21, 2025
updated 0.2.1 ← 0.1.0 diff Jul 6, 2021
new 0.1.0 Mar 11, 2021