spectralPamClusteringNg {uHMM}

spectralPamClusteringNg function

Description

Perform spectral classification on the similarity matrix of a dataset, using pam algorithm (a more robust version of K-means) on projected data.

Usage

spectralPamClusteringNg(similarity, K)

Arguments

similarity

matrix of similarity

K

number of clusters

Value

The function returns a list containing:

label

vector of cluster sequencing.

centres

matrix of cluster medoids (similar in concept to means, but medoids are members of the dataset) in the space of the K first normalised eigen vectors.

id.med

integer vector of indices giving the medoid observation numbers.

vecteursPropresProjK

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

valeursPropresK

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

vecteursPropres

matrix containing, in columns, eigen vectors of the similarity matrix.

valeursPropres

vector containing eigen values of the similarity matrix.

cluster.info

matrix, each row gives numerical information for one cluster. These are the cardinality of the cluster (number of observations), the maximal and average dissimilarity between the observations in the cluster and the cluster's medoid, the diameter of the cluster (maximal dissimilarity between two observations of the cluster), and the separation of the cluster (minimal dissimilarity between an observation of the cluster and an observation of another cluster).

References

Ng Andrew, Y., M. I. Jordan, and Y. Weiss. "On spectral clustering: analysis and an algorithm [C]." Advances in Neural Information Processing Systems (2001).

See Also

pam