najwalaabid / frequent-itemset-mining

Comparing the performance of two frequent itemset mining algorithms, eclat and fp-growth, on 6 datasets.

Frequent Itemset Mining: eclat vs fp-growth

Overview

Comparing the performance of two frequent itemset mining algorithms, eclat and fp-growth on 6 datasets:

• retail and accident (source)
• groceries (source)
• bats (source)
• abalone (source)
• house (source)
• adult (source)

The study aims to identify the key characteristics of the datasets affecting the performance of the two algorithms. The report presents a summary of the key findings along with supporting figures.

What's included

The repository includes:

• report.pdf: the report presenting key results and corresponding discussion
• /code: the code directory, including:
  • /datasets: a copy of the dataset files used in the study.
  • /output: directory used to save the output of the two miners and related figures.
  • helper.py: includes helper functions for running the experiments.
  • miner.py: helper code for running the two mining algorithms, eclat and fp-growth.
  • main.py: main file to run the miners and generate figures.

You can clone the repository and run the file 'main.py' to re-execute the experiments. You can use the report as a reference for interpreting the results.