mlpack_dbscan(1) | User Commands | mlpack_dbscan(1) |
mlpack_dbscan - dbscan clustering
mlpack_dbscan -i string [-e double] [-m int] [-N bool] [-s string] [-S bool] [-t string] [-V bool] [-a string] [-C string] [-h -v]
This program implements the DBSCAN algorithm for clustering using accelerated tree-based range search. The type of tree that is used may be parameterized, or brute-force range search may also be used.
The input dataset to be clustered may be specified with the '--input_file (-i)' parameter; the radius of each range search may be specified with the ’--epsilon (-e)' parameters, and the minimum number of points in a cluster may be specified with the '--min_size (-m)' parameter.
The '--assignments_file (-a)' and '--centroids_file (-C)' output parameters may be used to save the output of the clustering. '--assignments_file (-a)' contains the cluster assignments of each point, and '--centroids_file (-C)' contains the centroids of each cluster.
The range search may be controlled with the '--tree_type (-t)', '--single_mode (-S)', and '--naive (-N)' parameters. '--tree_type (-t)' can control the type of tree used for range search; this can take a variety of values: 'kd', 'r', ’r-star', 'x', 'hilbert-r', 'r-plus', 'r-plus-plus', 'cover', 'ball'. The ’--single_mode (-S)' parameter will force single-tree search (as opposed to the default dual-tree search), and ''--naive (-N)' will force brute-force range search.
An example usage to run DBSCAN on the dataset in 'input.csv' with a radius of 0.5 and a minimum cluster size of 5 is given below:
$ mlpack_dbscan --input_file input.csv --epsilon 0.5 --min_size 5
For further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your distribution of mlpack.
12 December 2020 | mlpack-3.4.2 |