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).
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