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Performing Jarvis-Patrick Clustering



The Jarvis-Patrick clustering algorithm is good for detecting chain-like or non-globular clusters. It partitions data into clusters, generating a set of non-overlapping clusters.

For further details, see Overview of Jarvis-Patrick Clustering.



1. Click a complete dataset in the Experiments navigator. The item is highlighted.

2. Click the Partitional Clustering toolbar icon , or select Partitional Clustering from the Clustering menu, or right-click the item and select Partitional Clustering from the shortcut menu. The Partitional Clustering parameters dialog is displayed.

3. Set the parameters.



Clustering Orientation

Cluster by Genes or Samples.

Distance Measurements Between Data Points

Type of distance measurement to use to determine how close two data points are to each other.


Set this parameter to Jarvis-Patrick.

Neighbors to Examine

This value must be at least 2.

Neighbors in Common

This value must be at least 1, and must not exceed the value of Neighbors to Examine.


4. Click OK. The Experiment Progress dialog is displayed. It is dynamically updated as the Jarvis-Patrick Clustering operation is performed. To cancel the Jarvis-Patrick operation, click the Cancel button.

Upon successful completion, a new item is added under the original item in the Experiments navigator.


Related Topics:

Distance Metrics Overview

Clustering Overview

Export Partitional Cluster