SD.clv: A Wrapper Function for the clv.SD Function and its Components

In BCA: Business and Customer Analytics

Description

Provides a wrapper to several function calls in the clv package needed to construct the SD index value for a clustering solution. The number of clusters that has the lowest value of the SD index represents the "best" solution under the criteria used to construct the SD index.

Usage

SD.clv(x, clus, alpha)

Arguments

x	A numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a dataframe with all numeric columns) used to construct the clustering solution.
clus	The cluster to which each row of x was assigned.
alpha	A weight to be placed on the average scattering of the clustering solution.

Details

The SD index corresponds to the weighted sum of the average "scattering" of points within clusters and the inverse of the total seperation between clusters. The average scattering measure is based on the average sum of the squared differences between a clusters centroid all the points in a cluster, while total seperation is measured by the sum of the squared distance between cluster centroids. A solution with a low average scattering and a low value of the inverse total seperation is considered to be better than a solution with higher levels of these two measures.

Value

A scalar SD index value for the clustering solution.

Author(s)

Dan Putler

References

M. Haldiki, Y. Batistakis, M. Vazirgiannis (2001), On Clustering Validation Techniques, Journal of Intelligent Information Systems, 17:2/3.

See Also

clv.SD

Examples

  data(iris)
  iris.data <- iris[,1:4]
  irisC3 <- kmeans(iris.data, centers=3, nstart=10)
  SD.clv(iris.data, clus=irisC3$cluster, alpha=0.1)

BCA documentation built on May 2, 2019, 1:26 p.m.