Apriori_for_ARM

The most important aspect of this assignment was to understand there may be a huge difference between what a paper claims and our implementation of that regarding the execution time. Not only the differences originate from the algorithm, but also from the data structure that we use in the implementation.

Hardware and Software Specifications:

I compiled this code in Ubuntu 18.04 (64-bit version) using GCC version 7.3.0 and I had Intel i5-8500 CPU. You may see different execution times using different OS, compiler, or hardware.

To compile the code, use this line in the terminal:

g++ -std=c++17 -O2 AprioriImplementation.cpp

To run the code and see all details (with proper dataset file, support and value):

./a.out 10Kdataset.txt 0.001 0.8

This is a brief report of measures and run times for each of the three datasets:

Results for ~10K transactions (10 items for min_sup, and 80% of conf):

9949 transactions, 488 different items, run time =~ 12.74 seconds.
number of 1 to 10 frequent itemsets: (472, 24085, 8049, 3786, 2329, 1115, 407, 111, 21, 2)
There are 161856 strong association rules overall.

Results for 1K transactions (10 items for min_sup, and 80% of conf):

1000 transactions, 475 different items, run time =~ 0.11 seconds.
number of 1 to 2 frequent itemsets: (334, 174)
There is no strong association rules in this case.

Results for 100 transactions (10 items for min_sup, and 80% of conf):

100 transactions, 366 different items, run time =~ 0.00 seconds.
number of 1 frequent itemsets: (10)
There is no strong association rules in this case.

** To my knowledge, "-O3" is not as stable and consistent as "-O2", also there may be some problems if you compile the code with another version of g++. I have seen cases (on a computer with different version of GCC) with some problems generating all the strong association rules.

** This code is designed in a way that can handle databases with .txt and .csv format (I need to evaluate my code using .csv files, so I add supporting of such format to the code). If the input is a text file, make sure to have '\n' at the end of each line. Also it should follow UTF-8 and Unix(LF) to work properly. Since there is a discrepancy between Windows and Linux for this matter, you may see problems if you use improper database file.