dual_tree_kmeans.hpp File Reference
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Classes | |
| class | DualTreeKMeans< MetricType, MatType, TreeType > |
| An algorithm for an exact Lloyd iteration which simply uses dual-tree nearest-neighbor search to find the nearest centroid for each point in the dataset. More... | |
Namespaces | |
| mlpack | |
Linear algebra utility functions, generally performed on matrices or vectors. | |
| mlpack::kmeans | |
K-Means clustering. | |
Typedefs | |
template < typename MetricType , typename MatType > | |
| using | CoverTreeDualTreeKMeans = DualTreeKMeans< MetricType, MatType, tree::StandardCoverTree > |
| A template typedef for the DualTreeKMeans algorithm with the cover tree type. More... | |
template < typename MetricType , typename MatType > | |
| using | DefaultDualTreeKMeans = DualTreeKMeans< MetricType, MatType > |
| A template typedef for the DualTreeKMeans algorithm with the default tree type (a kd-tree). More... | |
Functions | |
template < typename TreeType > | |
| void | HideChild (TreeType &node, const size_t child, const typename std::enable_if_t< !tree::TreeTraits< TreeType >::BinaryTree > *junk=0) |
| Utility function for hiding children. More... | |
template < typename TreeType > | |
| void | HideChild (TreeType &node, const size_t child, const typename std::enable_if_t< tree::TreeTraits< TreeType >::BinaryTree > *junk=0) |
| Utility function for hiding children. More... | |
template < typename TreeType > | |
| void | RestoreChildren (TreeType &node, const typename std::enable_if_t<!tree::TreeTraits< TreeType >::BinaryTree > *junk=0) |
| Utility function for restoring children to a non-binary tree. More... | |
template < typename TreeType > | |
| void | RestoreChildren (TreeType &node, const typename std::enable_if_t< tree::TreeTraits< TreeType >::BinaryTree > *junk=0) |
| Utility function for restoring children to a binary tree. More... | |
Detailed Description
An implementation of a Lloyd iteration which uses dual-tree nearest neighbor search as a black box. The conditions under which this will perform best are probably limited to the case where k is close to the number of points in the dataset, and the number of iterations of the k-means algorithm will be few.
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.
Definition in file dual_tree_kmeans.hpp.