Package org.nlpub.watset.graph
Graph processing and clustering.
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Interface Summary Interface Description Clustering<V> An instance of Clustering returns clusters after running the underlying algorithm.NodeWeighting<V,E> Node weighting for Chinese Whispers. -
Class Summary Class Description ChineseWhispers<V,E> Implementation of the Chinese Whispers algorithm.ComponentsClustering<V,E> A trivial clustering algorithm that treats every connected component as a cluster.EmptyClustering<V> A trivial clustering algorithm that returns no clusters.MarkovClustering<V,E> Naïve implementation of the Markov Clustering (MCL) algorithm.MarkovClustering.NormalizeVisitor Visitor that normalizes columns.MarkovClusteringOfficial<V,E> A wrapper for the official implementation of the Markov Clustering (MCL) algorithm in C.MaxMax<V,E> Implementation of the MaxMax soft clustering algorithm.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.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.Watset<V,E> Deprecated. useSimplifiedWatsetinstead.