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        "author": "Christopher J. MacLellan, Erik Harpstead",
        "author_email": "maclellan.christopher@gmail.com, whitill29@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
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        "description": "=================\nConcept Formation\n=================\n\nThis is a Python library of algorithms that perform concept formation written by\nChristopher MacLellan (http://www.christopia.net) and Erik Harpstead\n(http://www.erikharpstead.net). \n\nOverview\n========\n\nIn this library, the `COBWEB\n<http://axon.cs.byu.edu/~martinez/classes/678/Papers/Fisher_Cobweb.pdf>`_ and\n`COBWEB/3\n<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.4676&rep=rep1&type=pdf>`_\nalgorithms are implemented. These systems accept a stream of instances, which\nare represented as dictionaries of attributes and values (where values can be\nnominal for COBWEB and either numeric or nominal for COBWEB/3), and learns a\nconcept hierarchy. The resulting hierarchy can be used for clustering and\nprediction.\n\nThis library also includes\n`TRESTLE <http://christopia.net/data/articles/publications/maclellan-trestle-2016.pdf>`_,\nan extension of COBWEB and COBWEB/3 that support structured and relational data\nobjects. This system employs partial matching to rename new objects to align\nwith previous examples, then categorizes these renamed objects.\n\nLastly, we have extended the COBWEB/3 algorithm to support three key\nimprovements. First, COBWEB/3 now uses an `unbiased estimator\n<https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation>`_ to\ncalculate the standard deviation of numeric values. This is particularly useful\nin situations where the number of available data points is low. Second,\nCOBWEB/3 supports online normalization of the continuous values, which is\nuseful in situations where numeric values are on different scales and helps to\nensure that numeric values do not impact the model more than nominal values.\nFinally, it is assumed that there is some base noise in measuring continuous\nvalues, this noise insures that the probability of any one value never exceeds\n1, even when the standard deviation is small. \n\nInstallation\n============\n\nYou can install this software using pip::\n\n    pip install -U concept_formation\n\nYou can install the latest version of the code directly from github::\n\n    pip install -U git+https://github.com/cmaclell/concept_formation@master\n\nImportant Links\n===============\n\n- Source code: `<https://github.com/cmaclell/concept_formation>`_\n- Documentation: `<http://concept-formation.readthedocs.org>`_\n\nExamples\n========\n\nWe have created a number of examples to demonstrate the basic functionality of\nthis library. The examples can be found \n`here <http://concept-formation.readthedocs.io/en/latest/examples.html>`_.  \n\nCiting this Software \n====================\n\nIf you use this software in a scientific publiction, then we would appreciate\ncitation of the following paper:\n\nMacLellan, C.J., Harpstead, E., Aleven, V., Koedinger K.R. (2016) `TRESTLE: A\nModel of Concept Formation in Structured Domains\n<http://christopia.net/media/publications/maclellan-trestle-2016.pdf>`_.\nAdvances in Cognitive Systems, 4, 131-150.\n\nBibtex entry::\n\n    @article{trestle:2016a,\n    author={MacLellan, C.J. and Harpstead, E. and Aleven, V. and Koedinger, K.R.},\n    title={TRESTLE: A Model of Concept Formation in Structured Domains},\n    journal={Advances in Cognitive Systems},\n    volume={4},\n    year={2016}\n    }\n\n\n",
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