Clustering: A simple and powerful function to create clusters with KMeans

In MachineLearning: Machine Learning Algorithms for Innovation in Tourism

Description

This is a modified kmeans clustering technique to automatize the number of groups or clusters that can be partitioned the sample. Several techniques are used to obtain the best number of clusters.

Usage

Clustering(
  data,
  n = "auto",
  n_max = 10,
  iter.max = 10,
  auto_criterion = c("explainwss", "db", "ratkowsky", "ball", "friedman"),
  confidenceWSS = 0.9,
  agregate_method = median
)

Arguments

data	Data frame which numeric variables.
n	Data frame which numeric variables.
n_max	maximal number of clusters, between 2 and (number of objects - 1), greater or equal to n_min. By default, n_max=10.
iter.max	the maximum number of iterations allowed.
auto_criterion	the available criterions are: "explainwss", "db", "ratkowsky", "ball" and "friedman".
confidenceWSS	a confidence interval for criterion WSS.
agregate_method	a function to agregate results of different methods. Default value=median

Details

Several methods are available in order to obtain the best number of clusters: explainwss = Within-cluster Sum of Square db = Davies–Bouldin index (DBI). Davies and Bouldin (1979) ratkowsky = Ratkowsky and Lance (1978) ball = Ball and Hall (1965) friedman = Friedman and Rubin (1967)

@return A MLA object of subclass Clustering

Examples

## Load a Dataset
 ## Not run: 
data(EGATUR)
modelFit <- Clustering(data=EGATUR[,c("A13","gastototal")])

## End(Not run)

MachineLearning documentation built on March 13, 2020, 2:54 a.m.