Python package for finding Entropy-Based Distance Metric. An implementation of the following paper:

Y. Zhang, Y. Cheung and K. C. Tan, "A Unified Entropy-Based Distance Metric for Ordinal-and-Nominal-Attribute Data Clustering," in IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 1, pp. 39-52, Jan. 2020. doi: 10.1109/TNNLS.2019.2899381

Note EBDM requires Python 3.x

These instructions will get you a copy of the package up and running on your local machine for development and testing purposes.

Before getting started, make sure that you have the following libraries already installed:

import pandas as pd
import math

In your source file, import the library and start using the functions step-by-step as mentioned in the below section

import EBDM as ebd

For accessing modules, use

ebd.<module_name>

For reading the data, make sure that you’ve separated ordinal and nominal into separate CSV files.

Usage

nominal_features_dict = ebd.read_nom('nominal_data.csv')
ordinal_features_dict = ebd.read_ord('ordinal_data.csv')

Feel free to make contributions to this repository by submitting well-documented pull requests and raising issues.

To run the documentation website, open docs/_build/html/index.html

If you're making changes to the source code of docs folder, make sure that you compile a clean build in the shell using make clean; make html

• Rohit Jain - Rohit2706
• Ishita Mediratta - ishitamed19
• Ahsan Abbas - ahsanabbas123

This project is licensed under the MIT License - see the LICENSE.md file for details

• Sincere thanks to Dr. Mitali Mukerjee, Dr. Bhavana Prasher, Mr. Rintu Kutum and the AyurGenomics Group for guiding us throughout our internship period at the CSIR-IGIB, New Delhi.