FindNbrClusters: Find the optimal number of clusters
In l1spectral: An L1-Version of the Spectral Clustering

Description Usage Arguments Value Author(s) See Also Examples

View source: R/FindNbrClusters.R

This internal function of the l1-spectral algorithm finds the optimal number of clusters to build.

1	FindNbrClusters(A, structure, k = NULL, k_max = NULL)

`A`	The adjacency matrix
`structure`	Output of the function `FindStructure()`.
`k`	True number of clusters (not necessarily needed). If not provided, k is chosen by spectral eigengap.
`k_max`	Maximal number of clusters to form (not necessarily needed). If not provided, k_max is set to the number of nodes.

A list with the following elements:

nbr_clusters Optimal number of clusters by component,
nbr_clusters_total Optimal total number of clusters.

Camille Champion, Magali Champion

l1_spectralclustering, l1spectral.

 #########################################
 # Finding the optimal number of clusters
 #########################################

 # 1st example: non-perturbed graph
 Data <- CreateDataSet(k=3, n=20, p=list(p_inside=0,p_outside=0))

 Structure <- FindStructure(Data$A_hat)

 Clusters <- FindNbrClusters(A = Data$A_hat, structure = Structure, k=3)
 # The number of clusters is provided (3): each of the 3 components will be divided into 1 cluster

 Clusters <- FindNbrClusters(A = Data$A_hat, structure = Structure, k=5)
 # The number of clusters is provided (5) and larger than the number of components (3),
 # the spectral eigengap method is used to find the optimal number of clusters of each component.

 # 2nd example: perturbed graph
 Data <- CreateDataSet(k=3, n=20, p=list(p_inside=0.1,p_outside=0.1))

 Structure <- FindStructure(Data$A_hat) # there are less than 3 components

 Clusters <- FindNbrClusters(A = Data$A_hat, structure = Structure)
 # The number of clusters is optimized using the spectral eigengap method