MVR (version 1.20.0)

km.clustering: Wrapper Subroutine Around C Subroutine for 'K-means' Clustering Algorithm

Description

Perform 'K-means' clustering on a data matrix.

Usage

km.clustering(data, k, ns, maxiter)

Arguments

data

numeric matrix of data where variables are by columns and samples to cluster are by rows.

k

Positive integer scalar of number of centroids to start with.

ns

Positive integer scalar of how many random seedings should be done.

maxiter

Positive integer scalar of maximum number of iterations in 'K-means' clustering algorithm (convergence criteria).

Value

• lWk
  numeric scalar of Log-transformed within cluster dispersion statistic.
• centers
  numeric vector of cluster centers.
• membership
  numeric vector of cluster membership of each variable.
• obj
  Object of class similar to the one returned by "kmeans" in R package stats.

Details

The 'K-means' clustering algorithm (current default implementation) is derived from MacQueen, 1967.

References

• Dazard J-E., Hua Xu and J. S. Rao (2011). "R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization." In JSM Proceedings, Section for Statistical Programmers and Analysts. Miami Beach, FL, USA: American Statistical Association IMS - JSM, 3849-3863.
• Dazard J-E. and J. S. Rao (2012). "Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data." Comput. Statist. Data Anal. 56(7):2317-2333.
• MacQueen, J. (1967). "Some methods for classification and analysis of multivariate observations". In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam and J. Neyman, pp. 281-297. Berkeley, CA: University of California Press.

See Also

• sim.disSimilarity Statistic.
• MeanVarRegMean-Variance Regularization Core Function.
• withinsumsqWithin-Cluster Sum of Squares Distances.
• kmeans(R packagestats) for partition clustering in R.