Other packages > Find by keyword >

LearnClust  

Learning Hierarchical Clustering Algorithms
View on CRAN: Click here


Download and install LearnClust package within the R console
Install from CRAN:
install.packages("LearnClust")

Install from Github:
library("remotes")
install_github("cran/LearnClust")

Install by package version:
library("remotes")
install_version("LearnClust", "1.1")



Attach the package and use:
library("LearnClust")
Maintained by
Roberto Alcantara
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-09-30
Latest Update: 2020-11-29
Description:
Classical hierarchical clustering algorithms, agglomerative and divisive clustering. Algorithms are implemented as a theoretical way, step by step. It includes some detailed functions that explain each step. Every function allows options to get different results using different techniques. The package explains non expert users how hierarchical clustering algorithms work.
How to cite:
Roberto Alcantara (2020). LearnClust: Learning Hierarchical Clustering Algorithms. R package version 1.1, https://cran.r-project.org/web/packages/LearnClust. Accessed 06 Nov. 2024.
Previous versions and publish date:
1.0 (2020-09-30 11:30)
Other packages that cited LearnClust R package
View LearnClust citation profile
Other R packages that LearnClust depends, imports, suggests or enhances
Complete documentation for LearnClust
Functions, R codes and Examples using the LearnClust R package
Some associated functions: agglomerativeHC.details . agglomerativeHC . canberradistance.details . canberradistance . canberradistanceW.details . canberradistanceW . chebyshevDistance.details . chebyshevDistance . chebyshevDistanceW.details . chebyshevDistanceW . clusterDistance.details . clusterDistance . clusterDistanceByApproach.details . clusterDistanceByApproach . complementaryClusters.details . complementaryClusters . correlationHC.details . correlationHC . distances.details . distances . divisiveHC.details . divisiveHC . edistance.details . edistance . edistanceW.details . edistanceW . getCluster.details . getCluster . getClusterDivisive.details . getClusterDivisive . initClusters.details . initClusters . initData.details . initData . initImages . initTarget.details . initTarget . matrixDistance . maxDistance.details . maxDistance . mdAgglomerative.details . mdAgglomerative . mdDivisive.details . mdDivisive . mdistance.details . mdistance . mdistanceW.details . mdistanceW . minDistance.details . minDistance . newCluster.details . newCluster . normalizeWeight.details . normalizeWeight . octileDistance.details . octileDistance . octileDistanceW.details . octileDistanceW . toList.details . toList . toListDivisive.details . toListDivisive . usefulClusters . 
Some associated R codes: agglomerativeHC.R . agglomerativeHC.details.R . canberraDistance.R . canberraDistance.details.R . canberraDistanceW.R . canberraDistanceW.details.R . chebyshevDistance.R . chebyshevDistance.details.R . chebyshevDistanceW.R . chebyshevDistanceW.details.R . clusterDistance.R . clusterDistance.details.R . clusterDistanceByApproach.R . clusterDistanceByApproach.details.R . complementaryClusters.R . complementaryClusters.details.R . correlationHC.R . correlationHC.details.R . distances.R . distances.details.R . divisiveHC.R . divisiveHC.details.R . eDistanceW.R . eDistanceW.details.R . euclideanDistance.R . euclideanDistance.details.R . getCluster.R . getCluster.details.R . getClusterDivisive.R . getClusterDivisive.details.R . initClusters.R . initClusters.details.R . initData.R . initData.details.R . initImages.R . initTarget.R . initTarget.details.R . manhattanDistance.R . manhattanDistance.details.R . manhattanDistanceW.R . manhattanDistanceW.details.R . matrixDistance.R . maxDistance.R . maxDistance.details.R . mdAgglomerative.R . mdAgglomerative.details.R . mdDivisive.R . mdDivisive.details.R . minDistance.R . minDistance.details.R . newCluster.R . newCluster.details.R . normalizeWeight.R . normalizeWeight.details.R . octileDistance.R . octileDistance.details.R . octileDistanceW.R . octileDistanceW.details.R . toList.R . toList.details.R . toListDivisive.R . toListDivisive.details.R . usefulClusters.R .  Full LearnClust package functions and examples
Downloads during the last 30 days
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

maic  
Matching-Adjusted Indirect Comparison
A generalised workflow for generation of subject weights to be used in Matching-Adjusted Indirect C ...
Download / Learn more Package Citations See dependency  
memoise  
'Memoisation' of Functions
Cache the results of a function so that when you call it again with the same arguments it returns t ...
Download / Learn more Package Citations See dependency  
equatiomatic  
Transform Models into 'LaTeX' Equations
The goal of equatiomatic is to reduce the painassociated with writing LaTeX formulas from fitted mod ...
Download / Learn more Package Citations See dependency  
mailR  
A Utility to Send Emails from R
Interface to Apache Commons Email to send emails from R. ...
Download / Learn more Package Citations See dependency  
ddp  
Desirable Dietary Pattern
The desirable Dietary Pattern (DDP)/ PPH score measures the variety of food consumption. The (weigh ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  

23,092

R Packages

198,677

Dependencies

62,675

Author Associations

23,089

Publication Badges

© Copyright 2022 - present. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA