#include <mlpack/prereqs.hpp>#include <mlpack/core/util/io.hpp>#include <mlpack/core/util/mlpack_main.hpp>#include <mlpack/core/tree/binary_space_tree.hpp>#include <mlpack/core/tree/rectangle_tree.hpp>#include <mlpack/core/tree/cover_tree.hpp>#include <mlpack/methods/dbscan/random_point_selection.hpp>#include <mlpack/methods/dbscan/ordered_point_selection.hpp>#include "dbscan.hpp"
Include dependency graph for dbscan_main.cpp:
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Functions | |
| BINDING_NAME ("DBSCAN clustering") | |
| BINDING_SHORT_DESC ("An implementation of DBSCAN clustering. Given a dataset, this can " "compute and return a clustering of that dataset.") | |
| BINDING_LONG_DESC ("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 "+PRINT_PARAM_STRING("input")+" parameter; the radius of each range " "search may be specified with the "+PRINT_PARAM_STRING("epsilon")+" parameters, and the minimum number of points in a cluster may be " "specified with the "+PRINT_PARAM_STRING("min_size")+" parameter." "\" "The "+PRINT_PARAM_STRING("assignments")+" and "+PRINT_PARAM_STRING("centroids")+" output parameters may be " "used to save the output of the clustering. "+PRINT_PARAM_STRING("assignments")+" contains the cluster assignments of " "each point, and "+PRINT_PARAM_STRING("centroids")+" contains the " "centroids of each cluster." "\" "The range search may be controlled with the "+PRINT_PARAM_STRING("tree_type")+", "+PRINT_PARAM_STRING("single_mode")+", and "+PRINT_PARAM_STRING("naive")+" parameters. "+PRINT_PARAM_STRING("tree_type")+" 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 "+PRINT_PARAM_STRING("single_mode")+" parameter will force single-tree " "search (as opposed to the default dual-tree search), and '"+PRINT_PARAM_STRING("naive")+" will force brute-force range search.") | |
| BINDING_EXAMPLE ("An example usage to run DBSCAN on the dataset in "+PRINT_DATASET("input")+" with a radius of 0.5 and a minimum cluster size" " of 5 is given below:" "\"+PRINT_CALL("dbscan", "input", "input", "epsilon", 0.5, "min_size", 5)) | |
| BINDING_SEE_ALSO ("DBSCAN on Wikipedia", "https://en.wikipedia.org/wiki/DBSCAN") | |
| BINDING_SEE_ALSO ("A density-based algorithm for discovering clusters in large " "spatial databases with noise (pdf)", "http://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf") | |
| BINDING_SEE_ALSO ("mlpack::dbscan::DBSCAN class documentation", "@doxygen/classmlpack_1_1dbscan_1_1DBSCAN.html") | |
| PARAM_MATRIX_IN_REQ ("input", "Input dataset to cluster.", "i") | |
| PARAM_UROW_OUT ("assignments", "Output matrix for assignments of each " "point.", "a") | |
| PARAM_MATRIX_OUT ("centroids", "Matrix to save output centroids to.", "C") | |
| PARAM_DOUBLE_IN ("epsilon", "Radius of each range search.", "e", 1.0) | |
| PARAM_INT_IN ("min_size", "Minimum number of points for a cluster.", "m", 5) | |
| PARAM_STRING_IN ("tree_type", "If using single-tree or dual-tree search, the " "type of tree to use ('kd', 'r', 'r-star', 'x', 'hilbert-r', 'r-plus', " "'r-plus-plus', 'cover', 'ball').", "t", "kd") | |
| PARAM_STRING_IN ("selection_type", "If using point selection policy, the " "type of selection to use ('ordered', 'random').", "s", "ordered") | |
| PARAM_FLAG ("single_mode", "If set, single-tree range search (not dual-tree) " "will be used.", "S") | |
| PARAM_FLAG ("naive", "If set, brute-force range search (not tree-based) " "will be used.", "N") | |
| template<typename RangeSearchType , typename PointSelectionPolicy > | |
| void | RunDBSCAN (RangeSearchType rs, PointSelectionPolicy pointSelector=PointSelectionPolicy()) |
| template<typename RangeSearchType > | |
| void | ChoosePointSelectionPolicy (RangeSearchType rs=RangeSearchType()) |
Detailed Description
Implementation of program to run DBSCAN.
mlpack is free software; you may redistribute it and/or modify it under the terms of the 3-clause BSD license. You should have received a copy of the 3-clause BSD license along with mlpack. If not, see http://www.opensource.org/licenses/BSD-3-Clause for more information.
