This class implements K-Means clustering, using a variety of possible implementations of Lloyd's algorithm.
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| KMeans (const size_t maxIterations=1000, const MetricType metric=MetricType(), const InitialPartitionPolicy partitioner=InitialPartitionPolicy(), const EmptyClusterPolicy emptyClusterAction=EmptyClusterPolicy()) |
| Create a K-Means object and (optionally) set the parameters which K-Means will be run with. More...
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void | Cluster (const MatType &data, const size_t clusters, arma::Row< size_t > &assignments, const bool initialGuess=false) |
| Perform k-means clustering on the data, returning a list of cluster assignments. More...
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void | Cluster (const MatType &data, const size_t clusters, arma::mat ¢roids, const bool initialGuess=false) |
| Perform k-means clustering on the data, returning the centroids of each cluster in the centroids matrix. More...
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void | Cluster (const MatType &data, const size_t clusters, arma::Row< size_t > &assignments, arma::mat ¢roids, const bool initialAssignmentGuess=false, const bool initialCentroidGuess=false) |
| Perform k-means clustering on the data, returning a list of cluster assignments and also the centroids of each cluster. More...
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const EmptyClusterPolicy & | EmptyClusterAction () const |
| Get the empty cluster policy. More...
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EmptyClusterPolicy & | EmptyClusterAction () |
| Modify the empty cluster policy. More...
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size_t | MaxIterations () const |
| Get the maximum number of iterations. More...
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size_t & | MaxIterations () |
| Set the maximum number of iterations. More...
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const MetricType & | Metric () const |
| Get the distance metric. More...
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MetricType & | Metric () |
| Modify the distance metric. More...
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const InitialPartitionPolicy & | Partitioner () const |
| Get the initial partitioning policy. More...
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InitialPartitionPolicy & | Partitioner () |
| Modify the initial partitioning policy. More...
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template<typename Archive > |
void | Serialize (Archive &ar, const unsigned int version) |
| Serialize the k-means object. More...
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template<typename MetricType = metric::EuclideanDistance, typename InitialPartitionPolicy = SampleInitialization, typename EmptyClusterPolicy = MaxVarianceNewCluster, template< class, class > class LloydStepType = NaiveKMeans, typename MatType = arma::mat>
class mlpack::kmeans::KMeans< MetricType, InitialPartitionPolicy, EmptyClusterPolicy, LloydStepType, MatType >
This class implements K-Means clustering, using a variety of possible implementations of Lloyd's algorithm.
Four template parameters can (optionally) be supplied: the distance metric to use, the policy for how to find the initial partition of the data, the actions to be taken when an empty cluster is encountered, and the implementation of a single Lloyd step to use.
A simple example of how to run K-Means clustering is shown below.
* extern arma::mat data;
* arma::Row<size_t> assignments;
* arma::mat centroids;
*
* KMeans<> k;
* k.Cluster(data, 3, assignments, centroids);
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*
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* KMeans<metric::ManhattanDistance> k(100);
* k.Cluster(data, 6, centroids);
*
- Template Parameters
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MetricType | The distance metric to use for this KMeans; see metric::LMetric for an example. |
InitialPartitionPolicy | Initial partitioning policy; must implement a default constructor and either 'void Cluster(const arma::mat&, const size_t, arma::Row<size_t>&)' or 'void Cluster(const arma::mat&, const size_t, arma::mat&)'. |
EmptyClusterPolicy | Policy for what to do on an empty cluster; must implement a default constructor and 'void EmptyCluster(const arma::mat& data, const size_t emptyCluster, const arma::mat& oldCentroids, arma::mat& newCentroids, arma::Col<size_t>& counts, MetricType& metric, const size_t iteration)'. |
LloydStepType | Implementation of single Lloyd step to use. |
- See Also
- RandomPartition, SampleInitialization, RefinedStart, AllowEmptyClusters, MaxVarianceNewCluster, NaiveKMeans, ElkanKMeans
Definition at line 73 of file kmeans.hpp.
void Cluster |
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const MatType & |
data, |
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const size_t |
clusters, |
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arma::Row< size_t > & |
assignments, |
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arma::mat & |
centroids, |
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const bool |
initialAssignmentGuess = false , |
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const bool |
initialCentroidGuess = false |
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Perform k-means clustering on the data, returning a list of cluster assignments and also the centroids of each cluster.
Optionally, the vector of assignments can be set to an initial guess of the cluster assignments; to do this, set initialAssignmentGuess to true. Another way to set initial cluster guesses is to fill the centroids matrix with the centroid guesses, and then set initialCentroidGuess to true. initialAssignmentGuess supersedes initialCentroidGuess, so if both are set to true, the assignments vector is used.
- Template Parameters
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MatType | Type of matrix (arma::mat or arma::sp_mat). |
- Parameters
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data | Dataset to cluster. |
clusters | Number of clusters to compute. |
assignments | Vector to store cluster assignments in. |
centroids | Matrix in which centroids are stored. |
initialAssignmentGuess | If true, then it is assumed that assignments has a list of initial cluster assignments. |
initialCentroidGuess | If true, then it is assumed that centroids contains the initial centroids of each cluster. |