Ege Can Özer/Agustin Zuñiga See the project's main page for full documentation at: https://hel-dmp17.github.io/Apriori/
The project deals with the student-level data (ICPSR 4275) to find frequent itemsets and extract associaton rules. We are hoping to find social, educational, and cognitive relations. For instance, the students' performance, gender, family income, parents' education level may correlate with each other. In order to do that we are using Apriori algorithm to mine frequent itemsets. We also have provided some visualization methods, which are feasible to analyze the results.
We are using student-level data to study the classic data mining algorithm (Apriori). Briefly, the dataset approximately consists of 15000 records and 1600 features. However, we are focused only on 16 features. List of selected features could be seen in preprocessor.py file.
The dataset can be downloaded from the following link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4275
Arules is implemented in python 3.5.2 and depends on lxml library
You can install and run the program from scratch as follows
git clone https://github.com/HEL-DMP17/Apriori.git
cd Apriori
pip install -r requirements.txt
python arules.pyYou can check the all optional arguments using:
python arules.py -hAll optional arguments are as follows:
usage: arules.py [-h] [-f FILE] [-s SUPPORT] [-c CONFIDENCE]
optional arguments:
-h, --help show this help message and exit
-f FILE, --file FILE The dataset pathdata/samples.txt
-s SUPPORT, --support SUPPORT
The minimum support count, DEFAULT: 2.0
-c CONFIDENCE, --confidence CONFIDENCE
The minimum confidence, DEFAULT: 0.374