FastSpectralNJW: Jordan Fast Spectral Algorithm
In uHMM: Construct an Unsupervised Hidden Markov Model

Description Usage Arguments Details Value See Also Examples

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

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

`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).

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

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.

KmeansAutoElbow ZPGaussianSimilarity knn silhouette dunn connectivity dist

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)