Association-rule-mining

This project involves integrating text mining process, association rule mining, with exceptionality identification.

The text mining implementation and association rule extraction were stored in seperate files.

---

Datasets

The open-access thyroid disease-related dataset can be found through: UCI Machine Learning Repository.

The partial private dataset is available through Hospital_partial.csv

Please email xinyu.zhang@monash.edu for full access to the dataset.

---

Implementation Requirements

The overall flow of the project is as follows:

• Apply text mining procedures to extract key terminologies from raw digital medical reports.
• The specific text mining process was implemented through R, and in this case, we uses Chinese health reports for terminologies extraction.
• The overall text mining process is available through Text mining for PDF.R
• Store extracted terminologies into a .csv file
• Use the .csv file to apply association rule mining algorithms for rules extraction.
• The step-by-step common and exception rules generation procedures are available through Association rule mining with exceptionality.py
• Store or plot the generated rules.

TAHNK YOU!