gupta: Last Leap and Last Major Leap

In mattmail/clusterAnalysis: Several tools for determining the number of clusters in a dataset

Description

This method first computes the minimum distance between two cluster centers d. Then, it gets the optimal number of clusters according to the last leap method and the last major leap method.

Usage

gupta(X, maxK, clusterAlg = myKmean, verbose = FALSE, ...)

Arguments

X	data matrix or data frame of size n x d, n observations and d features
maxK	maximum number of cluster to evaluate
clusterAlg	clustering algorithm. Its output must be a list having an attribute "centers" containing the centers of each cluster. For more details, check the formatting of function myKmean.
verbose	logical, if TRUE it plot the evolution of the algorithm
...	additional parameters for the clustering algorithm

Value

list with 5 compoments:

d

vector of the minimum inter-center distance for each number of cluster

ll

vector containing the relative difference between d(k) and d(k+1)

ll_kopt

optimal number of clusters according to the last leap method

lml

vector indicating wether there is a major gap between values d(k) and d(k+1)

lml_kopt

optimal number of clusters according to the last major leap method

References

Gupta, A., Datta, S., and Das, S. (2018). Fast automatic estimation of the number of clusters from the minimum inter-center distancefor k-means clustering.Pattern Recognition Letters, 116.

mattmail/clusterAnalysis documentation built on Nov. 4, 2019, 6:18 p.m.