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"JarvisPatrick" (Machine Learning Method)

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" Automaticradius ϵ
    "SharedNeighborsNumber" Automaticminimum number of shared neighbors p

Examples

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Basic Examples  (2)Summary of the most common use cases

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

In[1]:=1
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https://wolfram.com/xid/0op7zszqqe15r0t4-20xbsr
Out[1]=1

Create random 2D vectors:

In[1]:=1
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https://wolfram.com/xid/0op7zszqqe15r0t4-q8s2rm
Out[1]=1

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

In[2]:=2
✖
https://wolfram.com/xid/0op7zszqqe15r0t4-fj7crm
Out[2]=2

Options  (5)Common values & functionality for each option

DistanceFunction  (1)

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

In[1]:=1
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https://wolfram.com/xid/0op7zszqqe15r0t4-868k7e
Out[1]=1

"NeighborhoodRadius"  (2)

Find clusters using "JarvisPatrick":

In[32]:=32
✖
https://wolfram.com/xid/0op7zszqqe15r0t4-040x9v
Out[32]=32

Find clusters by specifying the "NeighborhoodRadius" suboption:

In[33]:=33
✖
https://wolfram.com/xid/0op7zszqqe15r0t4-3656v8
Out[33]=33

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

In[1]:=1
✖
https://wolfram.com/xid/0op7zszqqe15r0t4-kqt0v0
Out[1]=1

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

In[2]:=2
✖
https://wolfram.com/xid/0op7zszqqe15r0t4-z3ek06
Out[2]=2

"SharedNeighborsNumber"  (2)

Find clusters by varying "NeighborsNumber" suboptions:

In[1]:=1
✖
https://wolfram.com/xid/0op7zszqqe15r0t4-0rrxoq
Out[1]=1

Create random 2D vectors:

In[1]:=1
✖
https://wolfram.com/xid/0op7zszqqe15r0t4-4onycb
Out[1]=1

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

In[2]:=2
✖
https://wolfram.com/xid/0op7zszqqe15r0t4-khjy3n
Out[2]=2

Applications  (1)Sample problems that can be solved with this function

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

In[51]:=51
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https://wolfram.com/xid/0op7zszqqe15r0t4-f8bvfg
Out[54]=54

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

In[55]:=55
✖
https://wolfram.com/xid/0op7zszqqe15r0t4-xpxr9m

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

In[57]:=57
✖
https://wolfram.com/xid/0op7zszqqe15r0t4-eh30f0
Out[59]=59