First Come Clustering Algorithm CFirstComeClustering

• First Come Clustering Algorithm CFirstComeClustering
  • Parameters
  • Sample

This simple clustering algorithm works with only one run through the data set. Each new vector is added to the nearest cluster, or if all the clusters are too far, a new cluster will be created for this vector. At the end, the clusters that are too small (smaller than MinClusterSizeRatio) are destroyed and their vectors reassigned to other clusters.

In NeoML the algorithm is implemented by the CFirstComeClustering 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 CFirstComeClustering::CParam structure.

• DistanceFunc — the distance function
• MinVectorCountForVariance — the smallest number of vectors in a cluster for which the variance is considered valid
• DefaultVariance — the default variance value (to be used when a cluster has less than MinVectorCountForVariance elements);
• Threshold — the distance threshold for a new cluster to be created
• MinClusterSizeRatio — the minimum number of vectors in a cluster (ratio of the total number of vectors, values from 0 to 1);
• MaxClusterCount — the maximum number of clusters (used to make sure the algorithm does not create too many clusters in the cases when the input data has great differences)

Sample

This sample shows how to use the first come clustering algorithm to clusterize the Iris Data Set:

void Clusterize( const IClusteringData& irisDataSet, CClusteringResult& result )
{
	CFirstComeClustering::CParam params;
	params.Threshold = 5;

	CFirstComeClustering firstCome( params );
	firstCome.Clusterize( irisDataSet, result );
}