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Jarvis-Patrick Clustering Overview



Jarvis-Patrick clustering is a clustering method based on similarity between neighbors. Similarity (or closeness) is determined by using a distance metric. One or more Neighbors in Common are used to judge the cluster membership of the objects under study. The function is deterministic and non-iterative.


Algorithm Properties



General clustering parameters, distance measurements between data points, and distance measurements between clusters are used to perform this procedure. In addition to these general clustering parameters, there are two parameters specific to the Jarvis-Patrick algorithm:

The first parameter, Neighbors to Examine, specifies how many of each item's neighbors to consider when counting the number of mutual neighbors shared with another item. This value must be at least 2. Lower values cause the algorithm to finish faster, but the final set of clusters will have many small clusters. Higher values cause the algorithm to take longer to finish, but may result in fewer clusters and clusters that form longer chains.

The second parameter, Neighbors in Common, specifies the minimum number of mutual nearest neighbors two items must have for them to be in the same cluster. This value must be at least 1, and must not exceed the value of the Neighbors to Examine parameter. Lower values result in clusters that are compact. Higher values result in clusters that are more dispersed.


Basic Procedure


In GeneLinker™, input provided to the algorithm is as follows:


When to Use The Jarvis-Patrick Algorithm

Use this algorithm when you need to work with non-globular clusters, when tight clusters might be discovered in larger loose clusters, when a deterministic partitional clustering result is desired, or when clustering speed is an issue since the algorithm is not iterative.


Related Topics:

Performing Jarvis-Patrick Clustering

Clustering Overview

Tutorial 3: Jarvis-Patrick Clustering