K-Means clustering. More...
Classes | |
| class | AllowEmptyClusters |
| Policy which allows K-Means to create empty clusters without any error being reported. More... | |
| class | DualTreeKMeans |
| 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... | |
| class | DualTreeKMeansRules |
| class | DualTreeKMeansStatistic |
| class | ElkanKMeans |
| class | HamerlyKMeans |
| class | KillEmptyClusters |
| Policy which allows K-Means to "kill" empty clusters without any error being reported. More... | |
| class | KMeans |
| This class implements K-Means clustering, using a variety of possible implementations of Lloyd's algorithm. More... | |
| class | MaxVarianceNewCluster |
| When an empty cluster is detected, this class takes the point furthest from the centroid of the cluster with maximum variance as a new cluster. More... | |
| class | NaiveKMeans |
| This is an implementation of a single iteration of Lloyd's algorithm for k-means. More... | |
| class | PellegMooreKMeans |
| An implementation of Pelleg-Moore's 'blacklist' algorithm for k-means clustering. More... | |
| class | PellegMooreKMeansRules |
| The rules class for the single-tree Pelleg-Moore kd-tree traversal for k-means clustering. More... | |
| class | PellegMooreKMeansStatistic |
| A statistic for trees which holds the blacklist for Pelleg-Moore k-means clustering (which represents the clusters that cannot possibly own any points in a node). More... | |
| class | RandomPartition |
| A very simple partitioner which partitions the data randomly into the number of desired clusters. More... | |
| class | RefinedStart |
| A refined approach for choosing initial points for k-means clustering. More... | |
| class | SampleInitialization |
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
K-Means clustering.
Typedef Documentation
◆ CoverTreeDualTreeKMeans
| using CoverTreeDualTreeKMeans = DualTreeKMeans<MetricType, MatType, tree::StandardCoverTree> |
A template typedef for the DualTreeKMeans algorithm with the cover tree type.
Definition at line 170 of file dual_tree_kmeans.hpp.
◆ DefaultDualTreeKMeans
| using DefaultDualTreeKMeans = DualTreeKMeans<MetricType, MatType> |
A template typedef for the DualTreeKMeans algorithm with the default tree type (a kd-tree).
Definition at line 164 of file dual_tree_kmeans.hpp.
Function Documentation
◆ HideChild() [1/2]
| void mlpack::kmeans::HideChild | ( | TreeType & | node, |
| const size_t | child, | ||
| const typename std::enable_if_t< !tree::TreeTraits< TreeType >::BinaryTree > * | junk = 0 |
||
| ) |
Utility function for hiding children.
This actually does something, and is called if the tree is not a binary tree.
Referenced by DualTreeKMeans< MetricType, MatType, TreeType >::DistanceCalculations().
◆ HideChild() [2/2]
| void mlpack::kmeans::HideChild | ( | TreeType & | node, |
| const size_t | child, | ||
| const typename std::enable_if_t< tree::TreeTraits< TreeType >::BinaryTree > * | junk = 0 |
||
| ) |
Utility function for hiding children.
This is called when the tree is a binary tree, and does nothing, because we don't hide binary children in this way.
◆ RestoreChildren() [1/2]
| void mlpack::kmeans::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.
Referenced by DualTreeKMeans< MetricType, MatType, TreeType >::DistanceCalculations().
◆ RestoreChildren() [2/2]
| void mlpack::kmeans::RestoreChildren | ( | TreeType & | node, |
| const typename std::enable_if_t< tree::TreeTraits< TreeType >::BinaryTree > * | junk = 0 |
||
| ) |
Utility function for restoring children to a binary tree.