FastSpectralNJW {uHMM}

Jordan Fast Spectral Algorithm

Description

Perform the Jordan spectral algorithm for large databases. Data are sampled, using K-means with Elbow criteria, before being classified.

Usage

FastSpectralNJW(data, nK = NULL, Kech = 2000, StopCriteriaElbow = 0.97,
  neighbours = 7, method = "", nb.iter = 10, uHMMinterface = FALSE,
  console = NULL, tm = NULL)

Arguments

data

numeric matrix or dataframe.

nK

number of clusters desired. If NULL, optimal number of clusters will be computed using gap criteria.

Kech

maximum number of representative points in sampled data.

StopCriteriaElbow

maximum (minimum ?) de variance expliquees des points representatifs souhaite.

neighbours

number of neighbours considered for the computation of local scale parameters.

method

string specifying the spectral classification method desired, either "PAM" (for spectral kmedoids) or "" (for "spectral kmeans").

nb.iter

number of iterations.

uHMMinterface

logical indicating whether the function is used via the uHMMinterface.

console

frame of the uHMM interface in which messages should be displayed (only if uHMMinterface=TRUE).

tm

a one row dataframe containing text to display in the uHMMinterface (only if uHMMinterface=TRUE).

Details

Algorithme de Jordan pour un grand jeu de donnees : echantillonage puis spectral

Value

The function returns a list containing:

sim

similarity matrix of representative points, multiplied by its transpose (ZPGaussianSimilarity).

label

vector of cluster sequencing.

gap

number of clusters.

labelElbow

vector of prototype sequencing.

vpK

matrix containing, in columns, the K first normalised eigen vectors of the data similarity matrix.

valp

vector containing the K first eigen values of the data similarity matrix.

echantillons

matrix of prototypes coordinates.

label.echantillons

vector containing the cluster of each prototype.

numSymbole

vector containing the nearest prototype of each data item.

See Also

KmeansAutoElbow ZPGaussianSimilarity knn silhouette dunn connectivity dist

Examples

x=(runif(1000)*4)-2;y=(runif(1000)*4)-2
keep<-which((x**2+y**2<0.5)|(x**2+y**2>1.5**2 & x**2+y**2<2**2 ))
data<-data.frame(x,y)[keep,]

cl<-FastSpectralNJW(data,2)
plot(data,col=cl$label)