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Clusters data using hierarchical single-link algorithm.

Name | Type | Range | Description | |
---|---|---|---|---|

inPoints | Point2DArray | Array of points to cluster | ||

inClusters | Integer* | 2 - + | Number of clusters to extract | |

inMaxDistance | Real* | 0.0 - | Maximum distance between two closest points in a cluster | |

outClusters | Point2DArrayArray | Resulting Point2D clusters |

### Remarks

If input parameter inClusters is not set, number of clusters is determined in the following way:

- if inMaxDistance is set then every two points of mutual distance smaller or equal to inMaxDistance belong to one cluster;
- if inMaxDistance is not set then an array of sorted edges' lengths of minimum spanning tree of inPoints is determined and
a pair of two subsequent elements which makes a maximum difference is found.
Maximum distance is defined as the smaller element of such a pair.

In other words number of clusters is the number of vertical lines in the dendrogram cut by a horizontal line that can transverse the maximum distance vertically without intersecting a cluster.

### Errors

This filter can throw an exception to report error. Read how to deal with errors in Error Handling.

List of possible exceptions:

Error type | Description |
---|---|

DomainError |
Desired number of clusters is greater than inPoints size in ClusterPoints2D_SingleLink. |

DomainError |
Filter input inPoints is empty in ClusterPoints2D_SingleLink. |

DomainError |
Too big inPoints array in ClusterPoints2D_SingleLink. |

### Complexity Level

This filter is available on **Expert** Complexity Level.

### See Also

- ClusterPoints2D – Clusters 2D points using K Means Clustering method.