dudahart2

vector of integers. Clustering into two clusters.

clustering

numeric between 0 and 1. Significance level (recommended
    to be small if this is used for estimating the number of clusters).

alpha

Duda-Hart test for whether a data set should be split into two
  clusters.

cluster

Various methods for clustering and cluster validation.
  Fixed point clustering. Linear regression clustering. Clustering by 
  merging Gaussian mixture components. Symmetric 
  and asymmetric discriminant projections for visualisation of the 
  separation of groupings. Cluster validation statistics
  for distance based clustering including corrected Rand index. 
  Cluster-wise cluster stability assessment. Methods for estimation of 
  the number of clusters: Calinski-Harabasz, Tibshirani and Walther's 
  prediction strength, Fang and Wang's bootstrap stability. 
  Gaussian/multinomial mixture fitting for mixed 
  continuous/categorical variables. Variable-wise statistics for cluster
  interpretation. DBSCAN clustering. Interface functions for many 
  clustering methods implemented in R, including estimating the number of
  clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
  mixtures. For an overview see package?fpc.

dudahart2: Duda-Hart test for splitting

Description

Usage

Arguments

Value

References

See Also

Examples