Hierarchical clustering by Identifier

This tool implements a variation of the hierarchical clustering methodology which guarantees features with different values in the identifier field will never be in the same cluster. In all other respects the input and output are identical to the Hierarchical clustering tool.

Input parameters

Input layer

The base point dataset. The selected points within this dataset will be clustered and written to the output dataset along with a cluster field.

Identifier field

Any field on the input feature set that contains identifiers for groups of features to be clustered together. If two features have a different identifier they will not be placed in the same cluster.

Cluster tolerance

The size of the cluster tolerance in projected units. What this means for the cluster depends on the linkage method and the cluster criterion.

Label field name

The name of the label field in the output dataset. By default it is "label". This field will be used to populate the id of the cluster.

Linkage method

The linkage method for points in the cluster. See the linkage docs for more detailed explanations of the values.

Linkage metric

The metric used to calculate the distance between the points in the cluster. By default it uses euclidean distance. See the pdist documentation for more detailed explanation of the values.

Cluster criterion

The cluster criterion used to build the cluster. By default set to "distance". For a more detailed explanation see the fcluster documentation.

Output parameters

Clustered features

The point dataset with the cluster IDs written to the label field. All other fields of the feature are preserved.