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DEPARTMENT OF COMPUTER SCIENCE
CPSC 473 – Introduction to Data Mining
Fall 2019
Assignment 2
Due Date: Nov 17, 2018 at 11:59PM
Notes: This assignment can be finished in Java, Python, C, or C++.
Zip all your files (code files, readme file, test data files, and others if needed) together into
one file named CPSC473_a#_yourlastname_yourfirstname_student#.zip (or .rar, or .7z,
or .tar), then submit this package to blackboard. Since this assignment is an implementation of frequent pattern mining algorithm, which is
the same as assignment 1, input and output requirements will also be the same as the
assignment 1.
Assignment Description:
The main task of this assignment is straightforward:
Implement an FP-growth algorithm
For this assignment, in the package that you submit to the blackboard, you should include your
implementation in a directory named FPgrowth.
Detail requirements for implementation (mostly the same as assignment 1):
1. Your program should be able to read transaction database from a .txt file.
2. The format of transaction database file will be the same as assignment 1.
3. All possible input variables should be entered by command-line arguments.
a. For example, if, 1) the program is named fpgrowth, 2) the file path of database is
./testdata.txt, 3) the minimum support threshold is set to 50%. The command to
run your program can be:
java fpgrowth ./testdata.txt 50
4. You should provide a README file for program compilation and execution.
5. For the output, a console output simply prints out the total number of frequent patterns.
a. E.g.:
|FPs| = 9
b. (Optional) You can also print out the execution time of your program.
6. Output all the frequent patterns into a .txt file named MiningResult.txt.
a. An example of the output file is provided as SampleOutput.txt. It is the mining
result of the small dataset that we used in class.
DEPARTMENT OF COMPUTER SCIENCE
CPSC 473 – Introduction to Data Mining
Fall 2019
Mark Distribution and Bonus Marks: The FP-growth 100% Bonus Marks (Described below) 10%
About bonus marks: To get the bonus marks of this assignment, you need to document any special enhancement
you included in the implementation. For example, you used some special implementation
tricks to deal with candidate generation, which, can improve the performance compare with
the traditional method.
The document does not have to be long, as long as you clearly explain a) what you did, and
b) why do you think this method can improve the performance.
The format of the document does not have to be formal, an informal report format should
be good enough.
The more special enhancements you include in your implementation the better chance you
will get full bonus marks. The document should be in PDF, and named as BonusMarkDocumentation.pdf.
Bonus marks will be added into your final grade calculation. So, yes, if you get it, your
will have some extra marks to be added into your final grade.
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