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Project description
DenGraph performs a densitybased graph clustering. The algorithm was proposed as an extension for DBSCAN to support overlapping clusters. The approach is based around the neighbourhood of a node. The neighbourhood is defined by the number of reachable nodes within a given distance. Therefore, large groups of items which are close to each other form clusters. As DenGraph is a nonpartitioning approach, isolated, distinct and uncommon items are left unclustered. Instead, they are treated as noise.
Quick Overview
To use dengraph for clustering your data, two steps are required:
 Your data must be provided via the dengraph.graph.Graph interface. See the dengraph.graphs module for appropriate containers and examples.
 The graph must be fed to dengraph.dengraph.DenGraphIO.
>>> from dengraph.graphs.distance_graph import DistanceGraph >>> from dengraph.dengraph import DenGraphIO >>> # Graph with defined nodes, edges from distance function >>> graph = DistanceGraph( ... nodes=(1, 2, 3, 4, 5, 10, 11, 13, 14, 15, 17, 22, 23, 24, 25, 28, 29, 30, 31), ... distance=lambda node_from, node_to: abs(node_from  node_to) ... ) >>> # Cluster the graph >>> clustered_data = DenGraphIO(graph, cluster_distance=2, core_neighbours=3).clusters >>> # And print clusters >>> for cluster in sorted(clustered_data, key=lambda clstr: min(clstr)): ... print(sorted(cluster)) [1, 2, 3, 4, 5] [11, 13, 14, 15, 17] [22, 23, 24, 25] [28, 29, 30, 31]
Further Information
At the moment, you must refer to the module and class documentation.
 See dengraph.dengraph.DenGraphIO for an explanation of clustering settings.
 See dengraph.graph.Graph for documentation of the graph interface.
Useful Classes
We provide several implementations and tools for the Graph interface:
 Create a graph from a list of nodes and a distance function via dengraph.graphs.distance_graph.DistanceGraph
 Create a graph from adjacency lists via dengraph.graphs.adjacency_graph.AdjacencyGraph
 Read a distance matrix to a graph via dengraph.graphs.graph_io.csv_graph_reader
Frequently Asked Questions
Why is there no DenGraphHO class?
We haven’t implemented that one yet. It’s on our Todo.
Why is there no DenGraph class?
The original DenGraph algorithm is nondeterministic for unordered graphs. Since border nodes can belong to only one cluster, the first cluster wins  results depend on iteration order. The DenGraphIO algorithm does not have this issue and performs equally well.
Why is DenGraphO the same class as DenGraphIO?
Algorithmically, DenGraphIO is basically DenGraphO plus the option to insert/remove/modify nodes/edges. In the static case (just initialisation), both are equivalent. At the moment, we don’t have any optimisations based on immutability of DenGraphO. The alias exists so that applications can distinguish between the two, possibly benefiting from future optimisations.
Acknowledgement
This module is based on several publications:
 [1] T. Falkowski, A. Barth, and M. Spiliopoulou, “DENGRAPH: A Densitybased Community Detection Algorithm,” presented at the IEEE/WIC/ACM International Conference on Web Intelligence (WI‘07), 2007, pp. 112–115.
 [2] T. Falkowski, A. Barth, and M. Spiliopoulou, “Studying community dynamics with an incremental graph mining algorithm,” AMCIS 2008 Proceedings, 2008.
 [3] N. Schlitter, T. Falkowski, and J. Lässig, “DenGraphHO  a densitybased hierarchical graph clustering algorithm.,” Expert Systems, vol. 31, no. 5, pp. 469–479, 2014.
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