class nltk.cluster.VectorSpaceClusterer(normalise=False, svd_dimensions=None)[source]

Abstract clusterer which takes tokens and maps them into a vector space. Optionally performs singular value decomposition to reduce the dimensionality.


__init__([normalise, svd_dimensions])
param normalise:
 should vectors be normalised to length 1
classification_probdist(vector) Classifies the token into a cluster, returning a probability distribution over the cluster identifiers.
classify_vectorspace(vector) Returns the index of the appropriate cluster for the vector.
cluster(vectors[, assign_clusters, trace])
cluster_name(index) Returns the names of the cluster at index.
cluster_names() Returns the names of the clusters.
cluster_vectorspace(vectors, trace) Finds the clusters using the given set of vectors.
likelihood(vector, label)
likelihood_vectorspace(vector, cluster) Returns the likelihood of the vector belonging to the cluster.
num_clusters() Returns the number of clusters.
vector(vector) Returns the vector after normalisation and dimensionality reduction