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

Run community detection algorithms on a nearest-neighbor (NN) graph within `clusterRows`

.

1 2 3 4 5 6 7 8 9 | ```
NNGraphParam(
shared = TRUE,
...,
cluster.fun = "walktrap",
cluster.args = list()
)
## S4 method for signature 'ANY,NNGraphParam'
clusterRows(x, BLUSPARAM, full = FALSE)
``` |

`shared` |
Logical scalar indicating whether a shared NN graph should be constructed. |

`...` |
Further arguments to pass to |

`cluster.fun` |
Function specifying the method to use to detect communities in the NN graph. The first argument of this function should be the NN graph and the return value should be a communities object. Alternatively, this may be a string containing the suffix of any igraph community detection algorithm.
For example, |

`cluster.args` |
Further arguments to pass to the chosen |

`x` |
A numeric matrix-like object where rows represent observations and columns represent variables. |

`BLUSPARAM` |
A NNGraphParam object. |

`full` |
Logical scalar indicating whether the graph-based clustering objects should be returned. |

To modify an existing NNGraphParam object `x`

,
users can simply call `x[[i]]`

or `x[[i]] <- value`

where `i`

is any argument used in the constructor.

The `NNGraphParam`

constructor will return a NNGraphParam object with the specified parameters.

The `clusterRows`

method will return a factor of length equal to `nrow(x)`

containing the cluster assignments.
If `full=TRUE`

, a list is returned with `clusters`

(the factor, as above) and `objects`

;
the latter is a list with `graph`

(the graph) and `communities`

(the output of `cluster.fun`

).

Aaron Lun

`makeSNNGraph`

and related functions, to build the graph.

`cluster_walktrap`

and related functions, to perform community detection.

1 2 3 | ```
clusterRows(iris[,1:4], NNGraphParam())
clusterRows(iris[,1:4], NNGraphParam(k=5))
clusterRows(iris[,1:4], NNGraphParam(cluster.fun="louvain"))
``` |

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