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]
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 May. 2025.
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
04/0604/0704/0804/0904/1004/1104/1204/1304/1404/1504/1604/1704/1804/1904/2004/2104/2204/2304/2404/2504/2604/2704/2804/2904/3005/0205/0305/0405/05Downloads for LearnClust024681012141618202224TrendBars

Today's Hot Picks in Authors and Packages

HuraultMisc  
Guillem Hurault Functions' Library
Contains various functions for data analysis, notably helpers and diagnostics for Bayesian modelling ...
Download / Learn more Package Citations See dependency  
rotations  
Working with Rotation Data
Tools for working with rotational data, including simulation from the most commonly used distributi ...
Download / Learn more Package Citations See dependency  
tbrf  
Time-Based Rolling Functions
Provides rolling statistical functions based on date and time windows instead of n-lagged observatio ...
Download / Learn more Package Citations See dependency  
tashu  
Analysis and Prediction of Bicycle Rental Amount
Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on spe ...
Download / Learn more Package Citations See dependency  
simfam  
Simulate and Model Family Pedigrees with Structured Founders
The focus is on simulating and modeling families with founders drawn from a structured population (f ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  

24,205

R Packages

207,311

Dependencies

65,312

Author Associations

24,206

Publication Badges

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