qkspeclust: qkernel spectral Clustering

In qkerntool: Q-Kernel-Based and Conditionally Negative Definite Kernel-Based Machine Learning Tools

Description

This is also a qkernel spectral clustering algorithm which uses three ways to assign labels after the laplacian embedding: kmeans, hclust and dbscan.

Usage

## S4 method for signature 'qkspecc'
qkspeclust(x, clustmethod = "kmeans",
         Nocent=NULL,iterations=NULL, hmethod=NULL,eps = NULL, MinPts = NULL)

Arguments

x	object of class qkspecc.
clustmethod	the strategy to use to assign labels in the embedding space. There are three ways to assign labels after the laplacian embedding: kmeans, hclust and dbscan.
Nocent	the number of clusters
iterations	the maximum number of iterations allowed for "kmeans".
hmethod	the agglomeration method for "hclust". This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).
eps	Reachability distance for "dbscan".
MinPts	Reachability minimum no. of points for "dbscan".

Details

The qkernel spectral clustering works by embedding the data points of the partitioning problem into the subspace of the eigenvectors corresponding to the k smallest eigenvalues of the graph Laplacian matrix. Using the simple clustering methods like kmeans, hclust and dbscan on the embedded points usually leads to good performance. It can be shown that qkernel spectral clustering methods boil down to graph partitioning.

Value

An S4 object of class qkspecc which extends the class vector containing integers indicating the cluster to which each point is allocated. The following slots contain useful information

clust	The cluster assignments
eVec	The corresponding eigenvector
eVal	The corresponding eigenvalues
xmatrix	The original data matrix
ymatrix	The real valued matrix of eigenvectors corresponding to the k smallest eigenvalues of the graph Laplacian matrix
cndkernf	The kernel function used

Author(s)

Yusen Zhang
[e-mail]

References

Andrew Y. Ng, Michael I. Jordan, Yair Weiss
On Spectral Clustering: Analysis and an Algorithm
Neural Information Processing Symposium 2001

See Also

qkernmatrix, cndkernmatrix, qkspecc-class, qkspecc

Examples

data("iris")
x=as.matrix(iris[ ,-5])

qspe <- qkspecc(x,kernel = "rbfbase", qpar = list(sigma = 90, q = 0.9),
                Nocent=3, normalize="symmetric", maxk=15,iterations=1200)
plot(x, col = clust(qspe))

qspec <- qkspeclust(qspe,clustmethod = "hclust", Nocent=3, hmethod="ward.D2")
plot(x, col = clust(qspec))
plot(qspec)

qkerntool documentation built on May 2, 2019, 6:11 a.m.