Clustering is a fundamental problem in science and engineering. Many classic methods such as *k*-means, Gaussian mixture models, and hierarchical clustering, however, employ greedy algorithms which can be entrapped in local minima, sometimes drastical suboptimal ones at that. Recently introduced convex relaxations of *k*-means and hierarchical clustering shrink cluster centroids toward one another and ensure a unique global minimizer. This R package provides two variable splitting methods

```
* Alternating Method of Multipliers (ADMM)
* Alternating Minimization Algorithm (AMA)
```

for solving this convex formulation of the clustering problem. We seek the centroids *u*_{i} that minimize

$\frac{1}{2} \sum_i || x_i - u_i||_2^2 + \gamma \sum_l w_{l} ||u_{l1} - u_{l2} ||$

Two penalty norms are currently supported: 1-norm and 2-norm.

Details on the algorithms are in the paper [Splitting Methods for Convex Clustering] (http://arxiv.org/abs/1304.0499) by Chi and Lange.

Previous takes on this formulation of clustering:

Just Relax and Come Clustering! A Convexification of k-Means Clustering by Lindsten, Ohlsson, and Ljung.

Clusterpath: An Algorithm for Clustering using Convex Fusion Penalties by Hocking, Joulin, Bach, and Vert.