pyjedai.clustering

Classes

AbstractClustering()
BestMatchClustering()	Implements the Best Match Clustering algorithm.
CenterClustering()	Implements the Center Clustering algorithm.
ConnectedComponentsClustering()	Creates the connected components of the graph.
CorrelationClustering()	Implements the Correlation Clustering algorithm.
CutClustering()	Implements the Cut Clustering algorithm.
EquivalenceCluster(data, flattened_cluster)
ExactClustering()	Implements an adapted, simplified version of the Exact THRESHOLD algorithm, introduced in "Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching", also referred in "BIGMAT: A Distributed Affinity-Preserving Random Walk Strategy for Instance Matching on Knowledge Graphs".
ExtendedSimilarityEdge(left_node, ...[, active])
KiralyMSMApproximateClustering()	Implements the Kiraly MSM Approximate Clustering algorithm.
MarkovClustering()	Implements the Markov Clustering algorithm.
MergeCenterClustering()	Implements the Merge Center Clustering algorithm.
RicochetCluster(center, members)
RicochetSRClustering()	Implements the Ricochet SR Clustering algorithm.
RowColumnClustering()	Implements the Row Column Clustering algorithm.
UniqueMappingClustering()	Prunes all edges with a weight lower than t, sorts the remaining ones in decreasing weight/similarity and iteratively forms a partition for the top-weighted pair as long as none of its entities has already been matched to some other.
Vertex(identifier[, edges])