WWCSS

A data matrix (data frame, data table, matrix, etc) containing only entries of class numeric (i.e. matrix of data nugget centers). Must contain the location, weight, and cluster assignment for each observation.

The number of possible clusters. Must be of class numeric.

The number of columns from the original dataset before clustering and without weight variable. Must be of class numeric.

This function computes the weighted within cluster sum of squares (WWCSS) for a set of cluster assignments provided to a dataset with some weighting scheme.

Creating, refining, and clustering data nuggets.
Data nuggets reduce a large dataset into a small collection of nuggets of
data, each containing a center (location), weight (importance), and scale
(variability) parameter. Data nugget centers are created by choosing
observations in the dataset which are as equally spaced apart as possible.
Data nugget weights are created by counting the number observations
closest to a given data nugget<e2><80><99>s center. We then say the data nugget
'contains' these observations and the data nugget center is recalculated
as the mean of these observations. Data nugget scales are created by
calculating the trace of the covariance matrix of the observations
contained within a data nugget divided by the dimension of the dataset.
Data nuggets are refined by 'splitting' data nuggets which have scales or
shapes (defined as the ratio of the two largest eigenvalues of the
covariance matrix of the observations contained within the data nugget)
deemed too large. Data nuggets are clustered by using a weighted form of
k-means clustering which uses both the centers and weights of data nuggets
to optimize the clustering assignments.

WWCSS: Weighted Within Cluster Sum of Squares

Description

Usage

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Details

References