org.openimaj.experiment.evaluation.cluster.processor

Interface Clusterer<DATA>

Type Parameters:

DATA -

All Known Subinterfaces:

DataClusterer<DATA,CLUSTER>, DistanceClusterer<CLUSTERS>, MultiviewSimilarityClusterer<CLUSTERS>, SimilarityClusterer<CLUSTERS>, SparseMatrixClusterer<CLUSTERS>, SpatialClusterer<CLUSTERTYPE,DATATYPE>

All Known Implementing Classes:

ByteKMeans, CachedDoubleSpectralClustering, ClusterLimitedIntRAC, ContectedComponentSimilarityClusterer, DistanceDBSCAN, DoubleKDTreeClusterer, DoubleKMeans, DoubleMultiviewSpectralClustering, DoubleNNDBSCAN, DoubleSpectralClustering, FeatureVectorKMeans, FloatKMeans, HierarchicalByteKMeans, HierarchicalDoubleKMeans, HierarchicalFloatKMeans, HierarchicalIntKMeans, HierarchicalLongKMeans, HierarchicalShortKMeans, IncrementalLifetimeSparseClusterer, IncrementalSparseClusterer, IntKMeans, IntRAC, IntRandomForest, LongKMeans, PreparedSpectralClustering, RandomByteClusterer, RandomClusterer, RandomDoubleClusterer, RandomFloatClusterer, RandomIntClusterer, RandomLongClusterer, RandomSetByteClusterer, RandomSetDoubleClusterer, RandomSetFloatClusterer, RandomSetIntClusterer, RandomSetLongClusterer, RandomSetShortClusterer, RandomShortClusterer, ShortKMeans, SimilarityDBSCAN, SparseMatrixDBSCAN, SphericalKMeans

public interface Clusterer<DATA>

Something which wraps the functionality of producing clusters

Author:

Sina Samangooei (ss@ecs.soton.ac.uk)

Method Summary

Modifier and Type	Method and Description
int[][]	performClustering(DATA data)

Method Detail

performClustering

int[][] performClustering(DATA data)

Parameters:

data -

Returns:

Given data items, cluster them by index