Metadata-Version: 2.1
Name: itpminer
Version: 0.0.4
Summary: Python implementation of ITPMiner algorithm
Home-page: https://github.com/chanyoungs/itpminer
Author: Local E
Author-email: csong@locale.app
License: MIT license
Description: # Inter-Transactional Patterns Miner(itpminer)
        
        [![image](https://img.shields.io/pypi/v/itpminer.svg)](https://pypi.python.org/pypi/itpminer)
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Local-eRewards/itpminer/blob/main/demo.ipynb)
        [![image](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        
        **Python implementation of ITPMiner algorithm**[[1]](#1)
        
        -   Free software: MIT license
        
        ## Features
        
        -   Mine frequent inter-transactional items
        -   Generate association rules between inter-transactional items
        -   Generate a network graph of association rules
        -   Type definitions provided for [mypy](http://www.mypy-lang.org/) type checker
        
        ## Example
        
        See also [demo.ipynb](demo.ipynb) or [Colab Notebook](https://colab.research.google.com/github/Local-eRewards/itpminer/blob/main/demo.ipynb). The code below is available on [demo.py](demo.py).
        
        ```python
        # Import itpminer and create a dummy database of inter transactions
        from itpminer.utils import association_rules, rules_graph
        from itpminer import itp_miner
        
        database = [
            ["a", "b"],
            ["a", "c", "d"],
            ["a"],
            ["a", "b", "c", "d"],
            ["a", "b", "d"],
            ["a", "d"]
        ]
        
        # Mine frequent inter-transactional patterns
        tree_dict, frequent_patterns_dict, frequent_patterns_list, frequent_patterns_dataframe = itp_miner(
            database=database)
        ```
        
        <p align="center">
        <img src="images/frequent_patterns.png" alt="frequent_patterns_dataframe" width="300"/>
        </p>
        
        ```python
        # Derive association rules from frequent patterns
        rules_dict, rules_display_dict, rules_dataframe = association_rules(
            tree_dict=tree_dict)
        ```
        
        <p align="center">
        <img src="images/association_rules.png" alt="rules_dataframe" width="800"/>
        </p>
        
        ```python
        # Plot a network graph between extended items
        rules_graph(rules_display_dict=rules_display_dict, rules_dict=rules_dict)
        ```
        
        <p align="center">
        <img src="images/rules_graph.png" alt="rules_graph" width="800"/>
        </p>
        
        ## Credits
        
        This package was created with [Cookiecutter](https://github.com/cookiecutter/cookiecutter) and the [giswqs/pypackage](https://github.com/giswqs/pypackage) project template.
        
        ## References
        
        <a id="1">[1]</a>
        Anthony J.T. Lee, Chun-Sheng Wang,
        An efficient algorithm for mining frequent inter-transaction patterns,
        Information Sciences,
        Volume 177, Issue 17,
        2007,
        Pages 3453-3476,
        ISSN 0020-0255,
        https://doi.org/10.1016/j.ins.2007.03.007.
        (https://www.sciencedirect.com/science/article/pii/S002002550700151X)
        Keywords: Association rules; Data mining; Inter-transaction patterns
        
Keywords: itpminer
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Description-Content-Type: text/markdown
