concept-formation 0.1.3

A library for doing incremental concept formation using algorithms in the COBWEB family

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 and COBWEB/3 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, 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 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

Documentation

Basic API documentation can be found at concept-formation.readthedocs.org.

Examples

We have created a number of examples to demonstrate the basic functionality of this library. Details on running these examples can be found here. Note, this library has no prerequisites, but some of the examples require Matplotlib and Scikit-learn.

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. 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}
}