Hierarchical.md

Hierarchical Clustering CHierarchicalClustering

• Hierarchical Clustering CHierarchicalClustering
  • Parameters
  • Sample

NeoML library provides a naive realization of upward hierarchical clustering.

The initial state has a cluster for every element. On each step, the two closest clusters are merged. Once the target number of clusters is reached, or all clusters are too far from each other to be merged, the process ends.

In NeoML algorithm is implemented by the CHierarchicalClustering class that provides the IClustering interface. Its Clusterize method is used to split the data set into clusters.

Parameters

The clustering parameters are described by the CHierarchicalClustering::CParam structure.

• DistanceType — distance function
• MaxClustersDistance — maximum distance at which the two clusters may be merged
• MinClustersCount — minimum number of clusters in the result

Sample

In this sample an input data set is split into two clusters:

void Clusterize( IClusteringData& data, CClusteringResult& result )
{
	CHierarchicalClustering::CParam params;
	params.DistanceType = DF_Euclid;
	params.MinClustersCount = 2;
	params.MaxClustersDistance = 10.f;

	CHierarchicalClustering hierarchical( params );
	hierarchical.Clusterize( data, result );
}