{
    "info": {
        "author": "Eileen Kuehn, Max Fischer",
        "author_email": "maxfischer2781@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: System :: Clustering"
        ],
        "description": "dengraph - Density-based Graph Clustering\n=========================================\n\n|travis| |codecov| |landscape|\n\nDenGraph performs a density-based graph clustering.\nThe algorithm was proposed as an extension for DBSCAN to support overlapping clusters.\nThe approach is based around the neighbourhood of a node.\nThe neighbourhood is defined by the *number* of reachable nodes within a given *distance*.\nTherefore, large groups of items which are close to each other form clusters.\nAs DenGraph is a non-partitioning approach, isolated, distinct and uncommon items are left unclustered.\nInstead, they are treated as noise.\n\nQuick Overview\n--------------\n\nTo use ``dengraph`` for clustering your data, two steps are required:\n\n- Your data must be provided via the ``dengraph.graph.Graph`` interface.\n  See the ``dengraph.graphs`` module for appropriate containers and examples.\n\n- The graph must be fed to ``dengraph.dengraph.DenGraphIO``.\n\n.. code:: python\n\n    >>> from dengraph.graphs.distance_graph import DistanceGraph\n    >>> from dengraph.dengraph import DenGraphIO\n    >>> # Graph with defined nodes, edges from distance function\n    >>> graph = DistanceGraph(\n    ...     nodes=(1, 2, 3, 4, 5, 10, 11, 13, 14, 15, 17, 22, 23, 24, 25, 28, 29, 30, 31),\n    ...     distance=lambda node_from, node_to: abs(node_from - node_to)\n    ... )\n    >>> # Cluster the graph\n    >>> clustered_data = DenGraphIO(graph, cluster_distance=2, core_neighbours=3).clusters\n    >>> # And print clusters\n    >>> for cluster in sorted(clustered_data, key=lambda clstr: min(clstr)):\n    ...     print(sorted(cluster))\n    [1, 2, 3, 4, 5]\n    [11, 13, 14, 15, 17]\n    [22, 23, 24, 25]\n    [28, 29, 30, 31]\n\nFurther Information\n-------------------\n\nAt the moment, you must refer to the module and class documentation.\n\n- See ``dengraph.dengraph.DenGraphIO`` for an explanation of clustering settings.\n\n- See ``dengraph.graph.Graph`` for documentation of the graph interface.\n\nUseful Classes\n..............\n\nWe provide several implementations and tools for the ``Graph`` interface:\n\n- Create a graph from a list of nodes and a distance function via ``dengraph.graphs.distance_graph.DistanceGraph``\n\n- Create a graph from adjacency lists via ``dengraph.graphs.adjacency_graph.AdjacencyGraph``\n\n- Read a distance matrix to a graph via ``dengraph.graphs.graph_io.csv_graph_reader``\n\nFrequently Asked Questions\n--------------------------\n\n- Why is there no ``DenGraphHO`` class?\n\n  We haven't implemented that one yet.\n  It's on our Todo.\n\n- Why is there no ``DenGraph`` class?\n\n  The original DenGraph algorithm is non-deterministic for unordered graphs.\n  Since border nodes can belong to only one cluster, the first cluster wins - results depend on iteration order.\n  The ``DenGraphIO`` algorithm does not have this issue and performs equally well.\n\n- Why is ``DenGraphO`` the same class as ``DenGraphIO``?\n\n  Algorithmically, ``DenGraphIO`` is basically ``DenGraphO`` *plus* the option to insert/remove/modify nodes/edges.\n  In the static case (just initialisation), both are equivalent.\n  At the moment, we don't have any optimisations based on immutability of ``DenGraphO``.\n  The alias exists so that applications can distinguish between the two, possibly benefiting from future optimisations.\n\nAcknowledgement\n---------------\n\nThis module is based on several publications:\n\n- [1] T. Falkowski, A. Barth, and M. Spiliopoulou, \"DENGRAPH: A Density-based Community Detection Algorithm,\" presented at the IEEE/WIC/ACM International Conference on Web Intelligence (WI'07), 2007, pp. 112\u2013115.\n- [2] T. Falkowski, A. Barth, and M. Spiliopoulou, \u201cStudying community dynamics with an incremental graph mining algorithm,\u201d AMCIS 2008 Proceedings, 2008.\n- [3] N. Schlitter, T. Falkowski, and J. L\u00e4ssig, \"DenGraph-HO - a density-based hierarchical graph clustering algorithm.,\" Expert Systems, vol. 31, no. 5, pp. 469\u2013479, 2014.\n\n\n.. |travis| image:: https://travis-ci.org/MaineKuehn/dengraph.svg?branch=master\n    :target: https://travis-ci.org/MaineKuehn/dengraph\n    :alt: Unit Tests\n\n.. |codecov| image:: https://codecov.io/gh/MaineKuehn/dengraph/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/MaineKuehn/dengraph\n  :alt: Code Coverage\n\n.. |landscape| image:: https://landscape.io/github/MaineKuehn/dengraph/master/landscape.svg?style=flat\n   :target: https://landscape.io/github/MaineKuehn/dengraph/master\n   :alt: Code Health\n",
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