All Classes

Class	Description
ABCFormat	Utilities for handling the ABC (source, target, weight) edge list format.
AlgorithmProvider<V,E>	A utility class that creates instances of the graph clustering algorithms.
Application	Watset command-line interface.
CachedNormalizedModifiedPurity<V>	Cached normalized modified purity evaluation measure for overlapping clustering.
ChineseWhispers<V,E>	Implementation of the Chinese Whispers algorithm.
Clustering<V>	An instance of Clustering returns clusters after running the underlying algorithm.
ComponentsClustering<V,E>	A trivial clustering algorithm that treats every connected component as a cluster.
ContextSimilarity<V>	A similarity measure between two bags-of-words that maps them to a number.
ContextSimilarity.DummyContextSimilarity<V>	A simple context similarity measure that always returns zero.
CosineContextSimilarity<V>	The classical cosine similarity measure for bags-of-words.
EmptyClustering<V>	A trivial clustering algorithm that returns no clusters.
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.NormalizeVisitor	Visitor that normalizes columns.
MarkovClusteringBinaryRunner<V,E>	A wrapper for the official implementation of the Markov Clustering (MCL) algorithm in C.
Maximizer	Utilities for searching arguments of the maxima of the function.
MaxMax<V,E>	Implementation of the MaxMax soft clustering algorithm.
Measurer<V,E>	A clustering algorithm performance measurement class.
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.
NodeWeighting.LabelNodeWeighting<V,E>	A trivial and not particularly useful node weighting approach that assigns the current node label as the weight.
NodeWeighting.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.
NodeWeighting.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.
NodeWeighting.TopNodeWeighting<V,E>	The node weighting approach that chooses the label with the highest total edge weight in the neighborhood.
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.
SingletonClustering<V,E>	A trivial clustering algorithm that puts every node in a separate cluster.
TogetherClustering<V,E>	A trivial clustering algorithm that puts every node together in a single large cluster.
Vectors	Utilities for mapping bags-of-words to real-valued vectors.
Watset<V,E>	Deprecated. use SimplifiedWatset instead.