Statistics: evalclusters

### Function Reference: `evalclusters`

statistics: eva = evalclusters (x, clust, criterion)
statistics: eva = evalclusters (…, `Name`, `Value`)

Create a clustering evaluation object to find the optimal number of clusters.

`evalclusters` creates a clustering evaluation object to evaluate the optimal number of clusters for data x, using criterion criterion. The input data x is a matrix with `n` observations of `p` variables. The evaluation criterion criterion is one of the following:

``CalinskiHarabasz``

to create a `CalinskiHarabaszEvaluation` object.

``DaviesBouldin``

to create a `DaviesBouldinEvaluation` object.

``gap``

to create a `GapEvaluation` object.

``silhouette``

to create a `SilhouetteEvaluation` object.

The clustering algorithm clust is one of the following:

``kmeans``

to cluster the data using `kmeans` with `EmptyAction` set to `singleton` and `Replicates` set to 5.

``linkage``

to cluster the data using `clusterdata` with `linkage` set to `Ward`.

``gmdistribution``

to cluster the data using `fitgmdist` with `SharedCov` set to `true` and `Replicates` set to 5.

If the criterion is `CalinskiHarabasz`, `DaviesBouldin`, or `silhouette`, clust can also be a function handle to a function of the form `c = clust(x, k)`, where x is the input data, k the number of clusters to evaluate and c the clustering result. The clustering result can be either an array of size `n` with `k` different integer values, or a matrix of size `n` by `k` with a likelihood value assigned to each one of the `n` observations for each one of the k clusters. In the latter case, each observation is assigned to the cluster with the higher value. If the criterion is `CalinskiHarabasz`, `DaviesBouldin`, or `silhouette`, clust can also be a matrix of size `n` by `k`, where `k` is the number of proposed clustering solutions, so that each column of clust is a clustering solution.

In addition to the obligatory x, clust and criterion inputs there is a number of optional arguments, specified as pairs of `Name` and `Value` options. The known `Name` arguments are:

``KList``

a vector of positive integer numbers, that is the cluster sizes to evaluate. This option is necessary, unless clust is a matrix of proposed clustering solutions.

``Distance``

a distance metric as accepted by the chosen clust. It can be the name of the distance metric as a string or a function handle. When criterion is `silhouette`, it can be a vector as created by function `pdist`. Valid distance metric strings are: `sqEuclidean` (default), `Euclidean`, `cityblock`, `cosine`, `correlation`, `Hamming`, `Jaccard`. Only used by `silhouette` and `gap` evaluation.

``ClusterPriors``

the prior probabilities of each cluster, which can be either `empirical` (default), or `equal`. When `empirical` the silhouette value is the average of the silhouette values of all points; when `equal` the silhouette value is the average of the average silhouette value of each cluster. Only used by `silhouette` evaluation.

``B``

the number of reference datasets generated from the reference distribution. Only used by `gap` evaluation.

``ReferenceDistribution``

the reference distribution used to create the reference data. It can be `PCA` (default) for a distribution based on the principal components of X, or `uniform` for a uniform distribution based on the range of the observed data. `PCA` is currently not implemented. Only used by `gap` evaluation.

``SearchMethod``

the method for selecting the optimal value with a `gap` evaluation. It can be either `globalMaxSE` (default) for selecting the smallest number of clusters which is inside the standard error of the maximum gap value, or `firstMaxSE` for selecting the first number of clusters which is inside the standard error of the following cluster number. Only used by `gap` evaluation.

Output eva is a clustering evaluation object.

See also: CalinskiHarabaszEvaluation, DaviesBouldinEvaluation, GapEvaluation, SilhouetteEvaluation

Source Code: evalclusters