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        "author": "Gregory Giecold",
        "author_email": "ggiecold@jimmy.harvard.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: POSIX",
            "Programming Language :: C",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Visualization"
        ],
        "description": "Cluster\\_Ensembles\r\n==================\r\n\r\nA package for combining multiple partitions into a consolidated\r\nclustering. The combinatorial optimization problem of obtaining such a\r\nconsensus clustering is reformulated in terms of approximation\r\nalgorithms for graph or hyper-graph partitioning.\r\n\r\nInstallation\r\n------------\r\n\r\nCluster\\_Ensembles is written in Python and in C. You need Python 2.7,\r\nits Standard Library and the following packages:\r\n\r\n-  numexpr (version 2.4.0 or later)\r\n\r\n-  NumPy (version 1.9.0 or any ulterior version)\r\n\r\n-  SciPy\r\n\r\n-  scikit-learn\r\n\r\n-  setuptools\r\n\r\n-  PyTables\r\n\r\nThe ``pip install Cluster_Ensembles`` command mentioned below should automatically detect and, if applicable, install or update any of the afore-mentioned dependencies.\r\n\r\nAs yet another prelimiary to running Cluster\\_Ensembles, you should also\r\nfollow the few more instructions below.\r\n\r\nOn CentOS, Fedora or some Red Hat Linux distribution:\r\n\r\n-  open a terminal console;\r\n\r\n-  type in: ``sudo dnf install glibc.i686``.\r\n\r\nThis will install the GNU C library that is required to run a 32-bit\r\nexecutable binary with a 64-bit Linux kernel. This executable is tasked\r\nwith hyper-graph partitioning. Skipping this step would result in a\r\n``bad ELF interpreter`` error message when subsequently trying to run\r\nthe Cluster\\_Ensembles package.\r\n\r\nOn a Debian or Ubuntu platform, the following commands should yield the\r\nsame outcome: \r\n\r\n-  open a terminal console;\r\n\r\n-  type in: ``sudo dpkg --add-architecture i386`` to add support for the \r\n   i386 architecture; \r\n\r\n-  enter: ``sudo apt-get install libc6:i386``.\r\n\r\nUpon completion of the steps outlined above, install Cluster\\_Ensembles\r\nby sending a request to the Python Package Index (PyPI) as follows: \r\n\r\n-  open a terminal console;\r\n\r\n-  enter ``pip install Cluster_Ensembles``.\r\n\r\nAny missing third-party dependency should be automatically resolved.\r\nPlease note that as part of the installation of this package, some code\r\nwritten in C that will later on be required by the Cluster\\_Ensembles\r\npackage to determine a graph partition is automatically compiled under\r\nthe hood and according to the specifications of your machine. You\r\ntherefore need to ensure availability of ``CMake`` and ``GNU make`` on\r\nyour operating system.\r\n\r\nUsage\r\n-----\r\n\r\nSay that you have an array of shape (M, N) where each row corresponds to a vector reporting the cluster label of each of the N samples comprising your dataset. It is possible that some of those samples have been left out of consideration from some of those M clusterings; in this case, the corresponding entry is tagged as NaN (``numpy.nan``).\r\n\r\nThe few lines below illustrate how to submit consensus clustering analysis such an cluster_runs (M, N) array of cluster labels. A vector holding the consensus clustering identities for each of the N samples in your dataset, ``consensus_clustering_labels``, is returned.\r\n\r\nPlease note that those M vectors of clustering labels can correspond to partitions of the samples into distinct numbers of overall clusters. Cluster_Ensembles therefore offers the possibility of seeking a consensus clustering from the aggregation of a clustering of your dataset into, say, 10 groups, another clustering of a fraction of your samples into 5 clusters, yet another partition of your dataset into 20 clusters, etc. Those choices are entirely up to you. Pretty much all that is required for Cluster_Ensembles is an array of clustering vectors.\r\n\r\n::\r\n\r\n    >>> import numpy as np\r\n    >>> import Cluster_Ensembles as CE\r\n    >>> cluster_runs = np.random.randint(0, 50, (50, 15000))\r\n    >>> consensus_clustering_labels = CE.cluster_ensembles(cluster_runs, verbose = True, N_clusters_max = 50)\r\n\r\nReferences\r\n----------\r\n\r\n-  Giecold, G., Marco, E., Trippa, L. and Yuan, G.-C., \r\n   \u201cRobust Lineage Reconstruction from High-Dimensional Single-Cell Data\u201d. \r\n   ArXiv preprint [q-bio.QM, stat.AP, stat.CO, stat.ML]: http://arxiv.org/abs/1601.02748\r\n\r\n-  A. Strehl and J. Ghosh, \"Cluster Ensembles - A Knowledge Reuse\r\n   Framework for Combining Multiple Partitions\". In: Journal of Machine\r\n   Learning Research, 3, pp. 583-617. 2002",
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        "keywords": "aggregation clustering consensus consensus-clustering CSPA data-mining ensemble ensemble-clustering HGPA hyper-graph machine-learning MCLA partition pattern-recognition unsupervised-learning",
        "license": "MIT License",
        "maintainer": "",
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        "name": "Cluster_Ensembles",
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