CRAN/E | naspaclust

naspaclust

Nature-Inspired Spatial Clustering

Installation

About

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) doi:10.1109/CITSM.2014.7042178 using Artificial Bee Colony algorithm.

Key Metrics

Version 0.2.1
R ≥ 3.5.0
Published 2021-07-07 1017 days ago
Needs compilation? no
License GPL-3
CRAN checks naspaclust results

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Maintainer

Maintainer

Bahrul Ilmi Nasution

bahrulnst@gmail.com

Authors

Bahrul Ilmi Nasution

aut / cre

Robert Kurniawan

aut

Rezzy Eko Caraka

aut

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

naspaclust archive

Depends

R ≥ 3.5.0

Imports

Rdpack
rdist
stabledist
beepr

Suggests

ppclust
spatialClust
cluster
ggplot2