You will propose your own project and work as a team of up to 3 for the final project.
You are encouraged to discuss with the instructor on your project ideas, plans and progress throughout the project period.
- Comparative study and/or implementation/evaluation of specific mining
- Data mining challenges (Below are some open and past challenges, you are also welcome to find your own)
- Propose your own project (discuss with instructor,
sample projects from previous classes).
- Project proposal (10 points)
- In class project presentation (10 points)
- Project report and deliverables (80 points)
- Project proposal due: 10/27/2017, 11:59pm
- In class project presentation: 11/27, 11/29 or 12/4
- Project report and deliverables due: 12/16/2017, 11:59pm
1. Project proposal should be roughtly 1-2 pages (in PDF) and contain the following content (when applicable):
- Motivation and objective: motivate the problem that you are investigating and summarize your goals.
- Related work/methods: review existing methods which may be applicable for your project.
- Proposed work: outline what you will do in the project.
- Evaluation: describe the datasets you will be using and your evaluation/testing plans.
- Plan of action: outline a weekly schedule of how progress will be made on the project and how the workload will be distributed among the team members if you work as a team
2. Project presentation should highlight the problem you are solving, your approach and methodolgy, results in progress, and a demo if available. Depending on the number of projects we will have in class, we will assign a presentation slot of 10-15 mintues for each project.
3. Project report should be roughtly 3-5 pages (in PDF) and contain the following content (when applicable):
- Motivation and contributions: motivate the problem that you are investigating and summarize your goals and contributions
- Related work/methods: review existing work/methods that may be applicable for your project
- Approach and methodology: describe your approach/solution including any algorithmic developments and/or prototype implementation and/or experimental methodology
- Evaluation and results: describe your datasets and experiment parameters, present and discuss your results.
- Conclusion and future work: a) discuss what you have learned through the project and what concepts and techniques you learned in class are used in the project; and b) discuss potential extensions and future work
4. Project deliverables should be submitted as a tar or zip file and should contain your source code, the executable, a readme file explaining how to compile/run your program, and any dataset (or link to the dataset) you have used in your project, if applicable.