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## 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.

Overview | Package | Class | Use | Tree | Deprecated | Index | Help
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