SSE: Sum of squared error (SSE) for cluster evaluation
In jhmadsen/ClustTools: Clustering evaluation package

Description Usage Arguments Details Value Author(s) References Examples

Function to evaluate clustering results with sum of squared error (SSE) by calculating the distance from cluster members to cluster centroids

1	SSE(dataset, clusterVector)

`dataset`	The dataset for which a sum of squared error and cluster centroids are returned
`clusterVector`	A vector of with integers indicating which cluster observations belong to

SSE computes the sum of squared error for clustering results, given a cluster vector. The smaller the squared error, the greater clustering results are achieved with respect to intra-cluster distance. SSE also return a matrix with cluster centroids.

`centroidMatrix`	A matrix of n-clusters x n-dimensions with cluster centroids
`clusterWithin`	A vector of n-clusters length with sum of squared error
`sumWithin`	Sum of squared error for all clusters, i.e. sum(clusterWithin)

Jacob H. Madsen

Tan, P.-N., Steinbach, M., Karpatne, A., & Kumar, V. (2005). Introduction to Data Mining (Second edition). ISBN: 978-03-213-2136-7

## Select a dataset to standardize and cluster
X <- scale(iris[,1:4])

## Cluster the dataset with a given number of clusters
cluster.obj <- hclust(dist(X), method='complete')

## Cut the hierarchical clustering tree
chosen.clusters <- cutree(cluster.obj, 3)

## Evaluate the clustering results with 'SSE' and 'SST'
clusters.SSE <- SSE(X, chosen.clusters)$sumWithin
clusters.SST <- SST(X)

## Calculate the r-squared for your cluster solution
rsq <- 1-(clusters.SSE/clusters.SST)

print(rsq)