All Classes

Class	Description
ABCFormat	Utilities for handling the ABC (source, target, weight) edge list format.
Application	Watset command-line interface.
Application.ParseStatus	Status of command-line argument parsing.
CachedNormalizedModifiedPurity<V>	Cached normalized modified purity evaluation measure for overlapping clustering.
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.
ClusteringAlgorithmBuilder<V,E,C extends org.jgrapht.alg.interfaces.ClusteringAlgorithm<V>>	A builder for constructing the ClusteringAlgorithm instances.
ClusteringAlgorithmProvider<V,E>	A utility class that creates instances of the graph clustering algorithms.
ComponentsClustering<V,E>	A trivial clustering algorithm that treats every connected component as a cluster.
ComponentsClustering.Builder<V,E>	Builder for ComponentsClustering.
ContextSimilarities	Useful implementations of ContextSimilarity.
ContextSimilarities.CosineContextSimilarity<V>	The classical cosine similarity measure for bags-of-words.
ContextSimilarities.DummyContextSimilarity<V>	A simple context similarity measure that always returns zero.
ContextSimilarity<V>	A similarity measure between two bags-of-words that maps them to a number.
EmptyClustering<V>	A trivial clustering algorithm that returns no clusters.
EmptyClustering.Builder<V,E>	Builder for EmptyClustering.
ILEFormat	Utilities for handling the ILE (identifier, length, elements) file format.
IndexedSense<V>	An integer sense identifier.
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.
MarkovClustering.InflateVisitor	Visitor that raises each element to the power of MarkovClustering.r.
MarkovClustering.NormalizeVisitor	Visitor that normalizes columns.
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.
Maximizer	Utilities for searching arguments of the maxima of the function.
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<V>	A MaxMax clustering.
MaxMaxClustering.MaxMaxClusteringImpl<V>	Default implementation of the MaxMax clustering.
Measurer<V,E>	A clustering algorithm performance measurement class.
Measurer.Measurement<V>	A performance measurement result.
Neighbors	Utilities for extracting neighborhood graphs and iterating over them.
NetworkXFormat	Utilities for handling pickled NetworkX graphs.
NodeWeighting<V,E>	Node weighting for Chinese Whispers.
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.
NodeWeightings.WeightingMode	Weighting modes.
NormalizedModifiedPurity<V>	Normalized modified purity evaluation measure for overlapping clustering.
Pairwise<V>	Pairwise precision, recall, and F-score for cluster evaluation.
PrecisionRecall	A wrapper for precision and recall values that computes F-score.
Sampling	Utilities for statistical evaluation of computational experiments.
Sense<V>	A monad that provides the wrapped value with a sense identifier.
SenseInduction<V,E>	A simple graph-based word sense induction approach that clusters node neighborhoods.
SimplifiedWatset<V,E>	A faster and simplified version of Watset that does not need a context similarity measure.
SimplifiedWatset.Builder<V,E>	Builder for SimplifiedWatset.
SimplifiedWatset.Implementation<V,E>	Actual implementation of Simplified Watset.
SingletonClustering<V,E>	A trivial clustering algorithm that puts every node in a separate cluster.
SingletonClustering.Builder<V,E>	Builder for SingletonClustering.
TogetherClustering<V,E>	A trivial clustering algorithm that puts every node together in a single large cluster.
TogetherClustering.Builder<V,E>	Builder for TogetherClustering.
Vectors	Utilities for mapping bags-of-words to real-valued vectors.
Watset<V,E>	Deprecated. Replaced with SimplifiedWatset
Watset.Builder<V,E>	Builder for Watset.
WatsetClustering<V>	A Watset clustering.