- Environment l OS: Windows, Mac OS, or Linux
- Languages: C++, Java, or Python (any version is ok)
- Goal: find association rules using the Apriori algorithm
- Requirements
The program must meet the following requirements:
- Execution file name: apriori.exe
- Execute the program with three arguments: minimum support, input file name, output file name n Example:
- Minimum support = 5%, input file name = input.txt, output file name = output.txt
- If you python, you are allowed to use apriori.py file instead of apripri.exe
- Input file format (.txt)
[item_id]t[item_id]
[item_id]t[item_id]t[item_id]t[item_id]t[item_id]
[item_id]t[item_id]t[item_id]t[item_id]
n Row: transaction n item_id is a numerical value
- There is no duplication of items in each transaction n Example:
- Output file format Figure 1. Input file example (.txt)
[item_set]t[associative_item_set]t[support(%)]t[confidence(%)]
[item_set]t[associative_item_set]t[support(%)]t[confidence(%)]
n [item_set]t[associative_item_set]: association rules with minimum support
- [item_set][associative_item_set]
- Use braces to represent item sets: {[item_id],[item_id],} (Important!!) l g., {0}, {0,4}, {0,3,1}
- Support: probability that a transaction contains [item_set] [associative_item_set]
- Confidence: conditional probability that a transaction having [item_set] also contains [associative_item_set] n The order of output is unimportant.
- The value of support and confidence should be rounded to two decimal places.
e.g., 24.631 rounded to two decimal places should become 24.63.
- An additional penalty will be imposed if you dont keep the output file format. n Example:
- Note: Please make sure to match the output format!
If the format is not correct, you cant get any score.

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