Data: [1:n,1:d] matrix of dataset to be clustered. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.
FirstLambda: Set 1 for quadratic penalty based algorithm, 0.4 for revised ADMM.
SecondLambda: The magnitude of grouping penalty.
Tau: Tuning parameter: tau, related to grouping penalty.
PlotIt: Default: FALSE, if TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
...: Further arguments for PRclust, enables also usage of [Pan et al., 2013].
Details
Parameters are rather challenging to choose.
Returns
List of - Cls: [1:n] numerical vector with n numbers defining the classification as the main output of the clustering algorithm. It has k unique numbers representing the arbitrary labels of the clustering.
Object: Object defined by clustering algorithm as the other output of this algorithm
References
[Pan et al., 2013] Pan, W., Shen, X., & Liu, B.: Cluster analysis: unsupervised learning via supervised learning with a non-convex penalty, The Journal of Machine Learning Research, Vol. 14(1), pp. 1865-1889. 2013.
[Wu et al., 2016] Wu, C., Kwon, S., Shen, X., & Pan, W.: A new algorithm and theory for penalized regression-based clustering, The Journal of Machine Learning Research, Vol. 17(1), pp. 6479-6503. 2016.
Author(s)
Michael Thrun
Note
Data matrix is internally transposed in order to fit the definition of the algorithm.