# Details & Suboptions

• "JarvisPatrick" is a neighbor-based clustering method. "JarvisPatrick" works for arbitrary cluster shapes and sizes. However, it is parameter sensitive, and can fail when clusters have different densities or are loosely connected.
• The following plots show the results of the "JarvisPatrick" method applied to toy datasets:
• The algorithm finds clusters based on the similarity of nearest neighbors of data points, and uses "shared nearest neighbors" as a measure of similarity between points.
• In "JarvisPatrick", neighbors are defined by points within a ball of ϵ radius. Each couple of neighbors that share at least p neighbors belong to the same cluster.
• The following suboptions can be given:
•  "NeighborhoodRadius" Automatic radius ϵ "SharedNeighborsNumber" Automatic minimum number of shared neighbors p

# Examples

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## Basic Examples(2)

Find clusters of nearby values using the "JarvisPatrick" method:

Create random 2D vectors:

Plot clusters in data found using the "JarvisPatrick" method:

## Options(5)

### DistanceFunction(1)

Find the clustering of the strings using the edit distance and specifying the "NeighborhoodRadius":

Find clusters using "JarvisPatrick":

Find clusters by specifying the "NeighborhoodRadius" suboption:

Define a set of two-dimensional data points, characterized by four somewhat nebulous clusters:

Plot different clusterings of the data using the "JarvisPatrick" method by varying the "NeighborhoodRadius":

### "SharedNeighborsNumber"(2)

Find clusters by varying "NeighborsNumber" suboptions:

Create random 2D vectors:

Plot clustering of data points using the "JarvisPatrick" method by varying the "SharedNeighborsNumber":

## Applications(1)

Create and visualize noisy 2D moon-shaped training and test datasets:

Train various ClassifierFunctions by varying the "NeighborhoodRadius" using the "JarvisPatrick" method:

Find and visualize different clusterings of the test set, given small changes in "NeighborhoodRadius":