Metadata-Version: 1.1
Name: Cluster_Ensembles
Version: 1.16
Summary: A package for determining the consensus clustering from an ensemble of partitions
Home-page: https://github.com/GGiecold/Cluster_Ensembles
Author: Gregory Giecold
Author-email: ggiecold@jimmy.harvard.edu
License: MIT License
Download-URL: https://github.com/GGiecold/Cluster_Ensembles
Description: Cluster\_Ensembles
        ==================
        
        A package for combining multiple partitions into a consolidated
        clustering. The combinatorial optimization problem of obtaining such a
        consensus clustering is reformulated in terms of approximation
        algorithms for graph or hyper-graph partitioning.
        
        Installation
        ------------
        
        Cluster\_Ensembles is written in Python and in C. You need Python 2.7,
        its Standard Library and the following packages: \* NumPy (version 1.9.0
        or any ulterior version); \* SciPy \* scikit-learn \* setuptools \*
        PyTables
        
        As yet another prelimiary to running Cluster\_Ensembles, you should also
        follow the few more instructions below.
        
        On CentOS, Fedora or some Red Hat Linux distribution: \* open a terminal
        console; \* type in: ``sudo dnf install glibc.i686``.
        
        This will install the GNU C library that is required to run a 32-bit
        executable binary with a 64-bit Linux kernel. This executable is tasked
        with hyper-graph partitioning. Skipping this step would result in a
        ``bad ELF interpreter`` error message when subsequently trying to run
        the Cluster\_Ensembles package.
        
        On a Debian or Ubuntu platform, the following commands should yield the
        same outcome: \* open a terminal console; \* type in:
        ``sudo dpkg --add-architecture i386`` to add the i386 architecture; \*
        enter: ``sudo apt-get install libc6:i386``.
        
        Upon completion of the steps outlined above, install Cluster\_Ensembles
        by sending a request to the Python Package Index (PyPI) as follows: \*
        open a terminal console; \* enter ``pip install Cluster_Ensembles``.
        
        Any missing third-party dependency should be automatically resolved.
        Please note that as part of the installation of this package, some code
        written in C that will later on be required by the Cluster\_Ensembles
        package to determine a graph partition is automatically compiled under
        the hood and according to the specifications of your machine. You
        therefore need to ensure availability of ``CMake`` and ``GNU make`` on
        your operating system.
        
        Usage
        -----
        
        ::
        
            >>> import numpy as np
            >>> import Cluster_Ensembles as CE
            >>> cluster_runs = np.random.randint(0, 50, (50, 15000))
            >>> consensus_clustering_labels = CE.cluster_ensembles(cluster_runs, verbose = True, N_clusters_max = 50)
        
        References
        ----------
        
        -  Giecold, G., Marco, E., Trippa, L. and Yuan, G.-C., "Robust Inference
           of Cell Lineages", to appear
        -  A. Strehl and J. Ghosh, "Cluster Ensembles - A Knowledge Reuse
           Framework for Combining Multiple Partitions". In: Journal of Machine
           Learning Research, 3, pp. 583-617. 2002
        
        IMPORTANT NOTICE
        ----------------
        
        A more detailed README file and expanded docstrings will be posted soon.
        
Keywords: aggregation clustering consensus consensus-clustering CSPA data-mining ensemble ensemble-clustering HGPA hyper-graph machine-learning MCLA partition pattern-recognition unsupervised-learning
Platform: Any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Programming Language :: C
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Mathematics
