Introduction to Data Mining - Problem set 1

Goal is to generate frequent item sets and find association rules with Apriori algorithm. See problem set description.

Technologies used in this project:

• Python 3.7
• GitLab CI
• Git

File Structure

├── main.py
├── test.py
├── problemset
│   ├── __init__.py
│   ├── parse
│   ├── generate
│   ├── utils
│   ├── interact

Resources

Here's the what I generally used, a research paper from 1994 by IBM.

The Apriori Principle:

If an itemset is frequent, then all of its subsets must also be frequent. Conversely, if an subset is infrequent, then all of its supersets must be infrequent, too.

Usage and notes

• To run, just run the main.py file at the root:

  $ python3 main.py

• problemset.parser accepts csv format which indicates type of the value at the first line. This way the value is itemized by making a hashable immutable tuple as (indicator, value).