Package org.nlpub.watset.graph

Graph processing and clustering.

Interface Summary

Interface	Description
ClusteringAlgorithmBuilder<V,E,C extends org.jgrapht.alg.interfaces.ClusteringAlgorithm<V>>	A builder for constructing the ClusteringAlgorithm instances.
MaxMaxClustering<V>	A MaxMax clustering.
NodeWeighting<V,E>	Node weighting for Chinese Whispers.
WatsetClustering<V>	A Watset clustering.

Class Summary

Class	Description
ChineseWhispers<V,E>	Implementation of the Chinese Whispers algorithm.
ChineseWhispers.Builder<V,E>	Builder for ChineseWhispers.
ChineseWhispers.Implementation<V,E>	Actual implementation of Chinese Whispers.
ComponentsClustering<V,E>	A trivial clustering algorithm that treats every connected component as a cluster.
ComponentsClustering.Builder<V,E>	Builder for ComponentsClustering.
EmptyClustering<V>	A trivial clustering algorithm that returns no clusters.
EmptyClustering.Builder<V,E>	Builder for EmptyClustering.
MarkovClustering<V,E>	Naïve implementation of the Markov Clustering (MCL) algorithm.
MarkovClustering.Builder<V,E>	Builder for MarkovClustering.
MarkovClustering.Implementation<V,E>	Actual implementation of Markov Clustering.
MarkovClusteringExternal<V,E>	A wrapper for the official implementation of the Markov Clustering (MCL) algorithm in C.
MarkovClusteringExternal.Builder<V,E>	Builder for MarkovClusteringExternal.
MarkovClusteringExternal.Implementation<V,E>	Actual implementation of the Markov Clustering wrapper.
MaxMax<V,E>	Implementation of the MaxMax soft clustering algorithm.
MaxMax.Builder<V,E>	Builder for MaxMax.
MaxMax.Implementation<V,E>	Actual implementation of MaxMax.
MaxMaxClustering.MaxMaxClusteringImpl<V>	Default implementation of the MaxMax clustering.
NodeEmbedding<V>	Coordinates of the graph node.
NodeWeightings	Useful implementations of NodeWeighting.
NodeWeightings.LabelNodeWeighting<V,E>	A trivial and not particularly useful node weighting approach that assigns the current node label as the weight.
NodeWeightings.LinearNodeWeighting<V,E>	The node weighting approach that chooses the label with the highest total edge weight in the neighborhood divided by the neighbor node degree.
NodeWeightings.LogNodeWeighting<V,E>	The node weighting approach that chooses the label with the highest total edge weight in the neighborhood divided by the logarithm of the neighbor node degree.
NodeWeightings.TopNodeWeighting<V,E>	The node weighting approach that chooses the label with the highest total edge weight in the neighborhood.
SenseInduction<V,E>	A simple graph-based word sense induction approach that clusters node neighborhoods.
SingletonClustering<V,E>	A trivial clustering algorithm that puts every node in a separate cluster.
SingletonClustering.Builder<V,E>	Builder for SingletonClustering.
SpectralClustering<V,E>	Spectral Clustering performs clustering of the graph's Spectral Embedding.
SpectralClustering.Builder<V,E>	Builder for SpectralClustering.
SpectralClustering.Implementation<V,E>	Actual implementation of Spectral Clustering.
TogetherClustering<V,E>	A trivial clustering algorithm that puts every node together in a single large cluster.
TogetherClustering.Builder<V,E>	Builder for TogetherClustering.
Watset<V,E>	Watset is a local-global meta-algorithm for fuzzy graph clustering.
Watset.Builder<V,E>	Builder for Watset.
Watset.Implementation<V,E>	Actual implementation of Simplified Watset.
WatsetClustering.WatsetClusteringImpl<V>	A Watset clustering that computes disambiguated contexts on demand.

Enum Summary

Enum	Description
NodeWeightings.WeightingMode	Weighting modes.