Description Usage Arguments Details Value Author(s) References See Also Examples

Implements the shared nearest neighbor clustering algorithm by Ertoz, Steinbach and Kumar (2003).

1 |

`x` |
a data matrix/data.frame (Euclidean distance is used), a
precomputed dist object or a kNN object created with |

`k` |
Neighborhood size for nearest neighbor sparsification to create the shared NN graph. |

`eps` |
Two objects are only reachable from each other if they share at
least |

`minPts` |
minimum number of points that share at least |

`borderPoints` |
should border points be assigned to clusters like in DBSCAN? |

`...` |
additional arguments are passed on to the k nearest neighbor
search algorithm. See |

**Algorithm:**

Constructs a shared nearest neighbor graph for a given k. The edge weights are the number of shared k nearest neighbors (in the range of

*[0, k]*).Find each points SNN density, i.e., the number of points which have a similarity of

`eps`

or greater.Find the core points, i.e., all points that have an SNN density greater than

`MinPts`

.Form clusters from the core points and assign border points (i.e., non-core points which share at least

`eps`

neighbors with a core point).

Note that steps 2-4 are equivalent to the DBSCAN algorithm (see `dbscan()`

)
and that `eps`

has a different meaning than for DBSCAN. Here it is
a threshold on the number of shared neighbors (see `sNN()`

)
which defines a similarity.

A object of class `general_clustering`

with the following
components:

`cluster ` |
A integer vector with cluster assignments. Zero indicates noise points. |

`type ` |
name of used clustering algorithm. |

`param ` |
list of used clustering parameters. |

Michael Hahsler

Levent Ertoz, Michael Steinbach, Vipin Kumar, Finding Clusters
of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data,
*SIAM International Conference on Data Mining,* 2003, 47-59.
doi: 10.1137/1.9781611972733.5

Other clustering functions:
`dbscan()`

,
`extractFOSC()`

,
`hdbscan()`

,
`jpclust()`

,
`optics()`

1 2 3 4 5 6 7 | ```
data("DS3")
# Out of k = 20 NN 7 (eps) have to be shared to create a link in the sNN graph.
# A point needs a least 16 (minPts) links in the sNN graph to be a core point.
# Noise points have cluster id 0 and are shown in black.
cl <- sNNclust(DS3, k = 20, eps = 7, minPts = 16)
plot(DS3, col = cl$cluster + 1L, cex = .5)
``` |

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