I am proud to annouce that FastKmedoids is on CRAN now.

Description

This package provides R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert and Peter J. Rousseeuw 2019.

Installation

install.packages("fastkmedoids")

cran status cran_downloads

Install from source code:

devtools::install_github("lixun910/fastkmedoids")

Examples

    # We use the demo data sets "USArrests"
    data("USArrests")
    df <- scale(USArrests)
    dv <- as.vector(dist(df)) # compute distance matrix (lower triangular part)
    n <- nrow(df)
    
    # PAM
    clusters <- pam(dv, n, k=3)
    clusters
    
    # FastPAM (use "LAB" initializer by default)
    clusters1 <- fastpam(dv, n, k=3)
    clusters1
    
    # FastPAM, specify "BUILD" as initializer
    clusters2 <- fastpam(dv, n, k=3, initializer="BUILD")
    clusters2
    
    # FastCLARA
    clusters3 <- fastclara(dv, n, k=3, numsamples = 5, sampling=0.25)
    clusters3
    
    # FastCLARANS
    clusters4 <- fastclarans(dv, n, k=3, numlocal=2, maxneighbor=0.025)
    clusters4

Details

The C++ Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) are ported from ELKI project (see http://elki-project.github.io/). To generate identical results, the random number generator, specifically the xorshift+ generator, is also ported. The results of this fastkmedoids R package should be the same with ELKI if using same initial seed for random number generator.

Besides FastPAM, FastCLARA and FastCLARANS, the classic algorithms, including PAM, CLARA and CLARANS, are also implemented. If interested in writing wrappers for these algorithms, please use the github repository: https://github.com/lixun910/fastkmedoids

All three algorithms take the distance matrix (lower triangular part, column wise storage) as input, which can be computed using dist() function in R (see the examples below). If using a pre-computed distance matrix, please transform it (lower triangular part, column wise storage) to a 1-dimensional array.

All three algorithms takes the same parameters as in ELKI. If the explanation of the input paramters is not clear, please refer to ELKI :

  • FastPAM: https://elki-project.github.io/releases/current/javadoc/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/KMedoidsFastPAM.html
  • FastCLARA: https://elki-project.github.io/releases/current/javadoc/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/FastCLARA.html
  • FastCLARANS: https://elki-project.github.io/releases/current/javadoc/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/FastCLARANS.html

The C++ code is a part of GeoDa (https://github.com/geodacenter/geoda) and libgeoda. If you are interested in a GUI version of this C++ implementation. You can download and use the free and cross-platform GeoDa software from https://geodacenter.github.io. The lab note of using K-Medoids in GeoDa is here: https://geodacenter.github.io/workbook/7c_clusters_3/lab7c.html#k-medoids

Author(s)

Xun Li Maintainer: Xun Li lixun910@gmail.com

References

Erich Schubert, Peter J. Rousseeuw “Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms” 2019 https://arxiv.org/abs/1810.05691

See Also

https://arxiv.org/abs/1810.05691