# MinimaxLinkageClustering: Minimax Linkage Hierarchical Clustering In FCPS: Fundamental Clustering Problems Suite

### Description

In the minimax linkage hierarchical clustering every cluster has an associated prototype element that represents that cluster [Bien/Tibshirani, 2011].

### Usage

```MinimaxLinkageClustering(DataOrDistances, ClusterNo = 0,
DistanceMethod="euclidean", ColorTreshold = 0,...)
```

### Arguments

 `DataOrDistances` [1:n,1:d] matrix of dataset to be clustered. It consists of n cases or d-dimensional data points. Every case has d attributes, variables or features. Alternatively, symmetric [1:n,1:n] distance matrix `ClusterNo` A number k which defines k different clusters to be build by the algorithm. `DistanceMethod` See `parDist`, for example 'euclidean','mahalanobis','manhatten' (cityblock),'fJaccard','binary', 'canberra', 'maximum'. Any unambiguous substring can be given. `ColorTreshold` Draws cutline w.r.t. dendogram y-axis (height), height of line as scalar should be given `...` In case of plotting further argument for `plot`, see `as.dendrogram`

### Value

List of

 `Cls` If, ClusterNo>0: [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. Otherwise for ClusterNo=0: NULL `Dendrogram` Dendrogram of hierarchical clustering algorithm `Object` Ultrametric tree of hierarchical clustering algorithm

Michael Thrun

### References

[Bien/Tibshirani, 2011] Bien, J., and Tibshirani, R.: Hierarchical Clustering with Prototypes via Minimax Linkage, The Journal of the American Statistical Association, Vol. 106(495), pp. 1075-1084, 2011.

`HierarchicalClustering`
```data('Hepta')