fastclara: FastCLARA

In fastkmedoids: Faster K-Medoids Clustering Algorithms: FastPAM, FastCLARA, FastCLARANS

Description

Clustering Large Applications (CLARA) with the improvements, to increase scalability in the number of clusters. This variant will also default to twice the sample size, to improve quality. (Schubert and Rousseeuw, 2019)

Usage

fastclara(
  rdist,
  n,
  k,
  maxiter = 0L,
  initializer = "LAB",
  fasttol = 1,
  numsamples = 5L,
  sampling = 0.25,
  independent = FALSE,
  seed = 123456789L
)

Arguments

rdist	The distance matrix (lower triangular matrix, column wise storage)
n	The number of observations
k	The number of clusters to produce
maxiter	The maximum number of iterations (default: 0)
initializer	Initializer: either "BUILD" (used in classic PAM) or "LAB" (linear approximative BUILD)
fasttol	Tolerance for fast swapping behavior (may perform worse swaps). Default: 1.0, which means to perform any additional swap that gives an improvement. When set to 0, it will only execute an additional swap if it appears to be independent (i.e., the improvements resulting from the swap have not decreased when the first swap was executed).
numsamples	Number of samples to draw (i.e. iterations). Default: 5
sampling	Sampling rate. Default value: 80 + 4*k. (see Schubert and Rousseeuw, 2019) If less than 1, it is considered to be a relative value. e.g. N*0.10
independent	NOT Keep the previous medoids in the next sample. Default: FALSE
seed	Seed for random number generator. Default: 123456789

Value

KMedoids S4 class

References

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

fastkmedoids documentation built on Jan. 22, 2021, 1:06 a.m.