qkspecc-class: Class "qkspecc"

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

Description

The qKernel Spectral Clustering Class

Objects from the Class

Objects can be created by calls of the form new("qkspecc", ...). or by calling the function qkspecc.

Slots

clust:

Object of class "vector" containing the cluster assignments

eVec:

Object of class "matrix" containing the corresponding eigenvector in each cluster

eVal:

Object of class "vector" containing the corresponding eigenvalue for each cluster

withinss:

Object of class "vector" containing the within-cluster sum of squares for each cluster

Methods

clust

signature(object = "qkspecc"): returns the cluster assignments

eVec

signature(object = "qkspecc"): returns the corresponding eigenvector in each cluster

eVal

signature(object = "qkspecc"): returns the corresponding eigenvalue for each cluster

xmatrix

signature(object = "qkspecc"): returns the original data matrix or a kernel Matrix

ymatrix

signature(object = "qkspecc"): returns The eigenvectors corresponding to the k smallest eigenvalues of the graph Laplacian matrix.

cndkernf

signature(object = "qkspecc"): returns the used kernel function

kcall

signature(object = "qkspecc"): returns the performed call

Author(s)

Yusen Zhang
[e-mail]

See Also

qkspecc, qkernel-class, cndkernel-class

Examples

## Cluster the iris data set.
data("iris")
x=as.matrix(iris[,-5])

qspe <- qkspecc(x,kernel = "rbfbase", qpar = list(sigma = 10, q = 0.9),
                Nocent=3, normalize="symmetric", maxk=15, iterations=1200)
clust(qspe)
eVec(qspe)
eVal(qspe)
xmatrix(qspe)
ymatrix(qspe)
cndkernf(qspe)

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