ds.kmeans: Conducts k-means clustering of a given data set

In FlorianSchw/dsClusterAnalysisClient: DataSHIELD client side functions for the dsClusterAnalysisClient package

Conducts k-means clustering of a given data set

Description

This function is similar to the native R function 'kmeans' from stats

Usage

ds.kmeans(
  df.name = NULL,
  clusters = NULL,
  iter.max = 10L,
  nstart = 1L,
  algorithm = "Hartigan-Wong",
  trace = FALSE,
  seed = 123,
  newobj = NULL,
  datasources = NULL
)

Arguments

df.name	is a string character of the data set
clusters	specifies the number of clusters for the computation
iter.max	specifies the max. number of iterations allowed
nstart	relates to the number of random sets if clusters is a number and not a set of initial cluster centers
algorithm	refers to the algorithm of calculating the kmeans and can be either 'Hartigan-Wong', 'Lloyd', 'Forgy' or 'MacQueen'
trace	is a logical or integer specifying whether tracing information on the progress of the algorithm is procuded for the Hartigan-Wong algorithm
seed	is an integer for setting the seed
newobj	is the name of the new object which is created with this function
datasources	a list of DSConnection-class objects obtained after login

Details

The function calls the server-side function kmeansDS that computes the k-means clustering of a data set (type 'data.frame' or 'matrix'). The function creates a new object on the server-side, which is of class 'kmeans'. The new object is named by the user using the newobj argument, otherwise it is named kmeans.newobj by default.

Value

the object specified by the newobj argument of ds.kmeans or default name kmeans.newobj

Author(s)

Florian Schwarz for the German Institute of Human Nutrition

FlorianSchw/dsClusterAnalysisClient documentation built on Feb. 8, 2025, 10:32 a.m.