Package edu.uci.ics.jung.algorithms.cluster

Provides a series of methods for locating clusters in graphs according to some model-based, heuristic or graph-theoretic criteria.

See:
Description

Interface Summary

GraphClusterer

Interface for finding clusters (sets of possibly overlapping vertices) in graphs.

Class Summary

BicomponentClusterer	Finds all biconnected components (bicomponents) of an undirected graph.
ClusterSet	A data structure representing the clusters, connected set of vertices (or edges), in a graph.
EdgeBetweennessClusterer	An algorithm for computing clusters (community structure) in graphs based on edge betweenness.
EdgeClusterSet	A ClusterSet where each cluster is a set of edge
ExactFlowCommunity	ExactFlowCommunity is an algorithm that uses a set of root nodes that are supposed to be representative of a community to find the entire community using principles based on max-flow/min-cut.
KMeansClusterer	Groups Objects into a specified number of clusters, based on their proximity in d-dimensional space, using the k-means algorithm.
VertexClusterSet	A ClusterSet where each cluster is a set of vertices
VoltageClusterer	Clusters vertices of a Graph based on their ranks as calculated by VoltageRanker.
WeakComponentClusterer	Finds all weak components in a graph where a weak component is defined as a maximal subgraph in which all pairs of vertices in the subgraph are reachable from one another in the underlying undirected subgraph.

Exception Summary

KMeansClusterer.NotEnoughClustersException

An exception that indicates that the specified data points cannot be clustered into the number of clusters requested by the user.

Package edu.uci.ics.jung.algorithms.cluster Description

Provides a series of methods for locating clusters in graphs according to some model-based, heuristic or graph-theoretic criteria.