Class VoltageClusterer<V,E>
java.lang.Object
edu.uci.ics.jung.algorithms.cluster.VoltageClusterer<V,E>
Clusters vertices of a Graph
based on their ranks as
calculated by VoltageScorer
. This algorithm is based on,
but not identical with, the method described in the paper below.
The primary difference is that Wu and Huberman assume a priori that the clusters
are of approximately the same size, and therefore use a more complex
method than k-means (which is used here) for determining cluster
membership based on co-occurrence data.
The algorithm proceeds as follows:
- first, generate a set of candidate clusters as follows:
- pick (widely separated) vertex pair, run VoltageScorer
- group the vertices in two clusters according to their voltages
- store resulting candidate clusters
- second, generate k-1 clusters as follows:
- pick a vertex v as a cluster 'seed'
(Wu/Huberman: most frequent vertex in candidate clusters) - calculate co-occurrence over all candidate clusters of v with each other vertex
- separate co-occurrence counts into high/low; high vertices constitute a cluster
- remove v's vertices from candidate clusters; continue
- pick a vertex v as a cluster 'seed'
- finally, remaining unassigned vertices are assigned to the kth ("garbage") cluster.
NOTE: Depending on how the co-occurrence data splits the data into clusters, the number of clusters returned by this algorithm may be less than the number of clusters requested. The number of clusters will never be more than the number requested, however.
- See Also:
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Nested Class Summary
Nested Classes -
Field Summary
Fields -
Constructor Summary
ConstructorsConstructorDescriptionVoltageClusterer
(Graph<V, E> g, int num_candidates) Creates an instance of a VoltageCluster with the specified parameters. -
Method Summary
Modifier and TypeMethodDescriptionprotected void
addOneCandidateCluster
(LinkedList<Set<V>> candidates, Map<V, double[]> voltage_ranks) alternative to addTwoCandidateClusters(): cluster vertices by voltages into 2 clusters.protected void
addTwoCandidateClusters
(LinkedList<Set<V>> candidates, Map<V, double[]> voltage_ranks) Do k-means with three intervals and pick the smaller two clusters (presumed to be on the ends); this is closer to the Wu-Huberman method.Collection
<Set<V>> cluster
(int num_clusters) Clusters the vertices ofg
intonum_clusters
clusters, based on their connectivity.protected Collection
<Set<V>> cluster_internal
(V origin, int num_clusters) Does the work ofgetCommunity
andcluster
.Collection
<Set<V>> getCommunity
(V v) getObjectCounts
(Collection<Set<V>> candidates, V seed) getSeedCandidates
(Collection<Set<V>> candidates) Returns a list of cluster seeds, ranked in decreasing order of number of appearances in the specified collection of candidate clusters.protected void
setRandomSeed
(int random_seed)
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Field Details
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num_candidates
protected int num_candidates -
kmc
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rand
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g
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Constructor Details
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VoltageClusterer
Creates an instance of a VoltageCluster with the specified parameters. These are mostly parameters that are passed directly to VoltageScorer and KMeansClusterer.- Parameters:
g
- the graph whose vertices are to be clusterednum_candidates
- the number of candidate clusters to create
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Method Details
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setRandomSeed
protected void setRandomSeed(int random_seed) -
getCommunity
- Parameters:
v
- the vertex whose community we wish to discover- Returns:
- a community (cluster) centered around
v
.
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cluster
Clusters the vertices ofg
intonum_clusters
clusters, based on their connectivity.- Parameters:
num_clusters
- the number of clusters to identify- Returns:
- a collection of clusters (sets of vertices)
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cluster_internal
Does the work ofgetCommunity
andcluster
.- Parameters:
origin
- the vertex around which clustering is to be donenum_clusters
- the (maximum) number of clusters to find- Returns:
- a collection of clusters (sets of vertices)
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addTwoCandidateClusters
protected void addTwoCandidateClusters(LinkedList<Set<V>> candidates, Map<V, double[]> voltage_ranks) Do k-means with three intervals and pick the smaller two clusters (presumed to be on the ends); this is closer to the Wu-Huberman method.- Parameters:
candidates
- the list of clusters to populatevoltage_ranks
- the voltage values for each vertex
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addOneCandidateCluster
alternative to addTwoCandidateClusters(): cluster vertices by voltages into 2 clusters. We only consider the smaller of the two clusters returned by k-means to be a 'true' cluster candidate; the other is a garbage cluster.- Parameters:
candidates
- the list of clusters to populatevoltage_ranks
- the voltage values for each vertex
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getSeedCandidates
Returns a list of cluster seeds, ranked in decreasing order of number of appearances in the specified collection of candidate clusters.- Parameters:
candidates
- the set of candidate clusters- Returns:
- a set of cluster seeds
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getObjectCounts
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