Metadata-Version: 1.0
Name: concept_formation
Version: 0.2.14
Summary: A library for doing incremental concept formation using algorithms in the COBWEB family.
Home-page: http://pypi.python.org/pypi/concept_formation/
Author: Christopher J. MacLellan, Erik Harpstead
Author-email: maclellan.christopher@gmail.com, whitill29@gmail.com
License: LICENSE.txt
Description: =================
        Concept Formation
        =================
        
        This is a Python library of algorithms that perform concept formation written by
        Christopher MacLellan (http://www.christopia.net) and Erik Harpstead
        (http://www.erikharpstead.net). 
        
        Overview
        ========
        
        In this library, the `COBWEB
        <http://axon.cs.byu.edu/~martinez/classes/678/Papers/Fisher_Cobweb.pdf>`_ and
        `COBWEB/3
        <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.4676&rep=rep1&type=pdf>`_
        algorithms are implemented. These systems accept a stream of instances, which
        are represented as dictionaries of attributes and values (where values can be
        nominal for COBWEB and either numeric or nominal for COBWEB/3), and learns a
        concept hierarchy. The resulting hierarchy can be used for clustering and
        prediction.
        
        This library also includes
        `TRESTLE <http://christopia.net/data/articles/publications/maclellan1-2015.pdf>`_,
        an extension of COBWEB and COBWEB/3 that support structured and relational data
        objects. This system employs partial matching to rename new objects to align
        with previous examples, then categorizes these renamed objects.
        
        Lastly, we have extended the COBWEB/3 algorithm to support two key
        improvements. First, COBWEB/3 now uses an `unbiased estimator
        <https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation>`_ to
        calculate the standard deviation of numeric values. This is particularly useful
        in situations where the number of available data points is low. Second,
        COBWEB/3 supports online normalization of the continuous values, which is
        useful in situations where numeric values are on different scales and helps to
        ensure that numeric values do not impact the model more than nominal values.
        
        Installation
        ============
        
        You can install this software using pip::
        
            pip install -U concept_formation
        
        You can install the latest version of the code directly from github::
            
            pip install -U git+https://github.com/cmaclell/concept_formation@master
        
        Important Links
        ===============
        
        - Source code: `<https://github.com/cmaclell/concept_formation>`_
        - Documentation: `<http://concept-formation.readthedocs.org>`_
        
        Examples
        ========
        
        We have created a number of examples to demonstrate the basic functionality of
        this library. The examples can be found 
        `here <http://concept-formation.readthedocs.org>`_.  
        
        Citing this Software 
        ====================
        
        If you use this software in a scientific publiction, then we would appreciate
        citation of the following paper:
        
        MacLellan, C.J., Harpstead, E., Aleven, V., Koedinger, K.R. (2015) `TRESTLE:
        Incremental Learning in Structured Domains using Partial Matching and
        Categorization <http://christopia.net/data/articles/publications/maclellan1-2015.pdf>`_.
        The Third Annual Conference on Advances in Cognitive Systems.
        Atlanta, GA. May 28-31, 2015.
        
        Bibtex entry::
        
            @inproceedings{trestle:2015a,
            author={MacLellan, C.J. and Harpstead, E. and Aleven, V. and Koedinger, K.R.},
            title={TRESTLE: Incremental Learning in Structured Domains using Partial
                   Matching and Categorization.},
            booktitle = {The Annual Third Conference on Advances in Cognitive Systems},
            year={2015}
            }
        
Platform: UNKNOWN
