About...
©1999 Osmar R. Zaïane
(zaiane@cs.ualberta.ca)
Activities
| Assignments | ||||||||
There are two assignments scheduled. The first one is a group work for
data mining tool evaluation. One tool from a set of data mining tools
is studied and evaluated, then presented in class. The second
assignments, a set of questions and exercices to answer, is is NOT a group work.
Important Dates
Homeworks will count 10% of the overall grade. |
| Projects | ||||||||
Implementation ProjectsIt is the student's responsability to choose or come up with an implementation project. The project can be the implementation of a new algorithm, the adaptation of an existing algorithm or combination of few existing algorithms to solve a given problem, a data visualization solution, etc.Students should write a project proposal (1 or 2 pages) explaining their project: topic, implementation choices, approach and schedule. All projects will be demo'ed to the class at the end of the semester. The implementations could be with C/C++ or Java, on Linux, Window NT/98 , or other systems. A project report should be submited at the end the project before the demo. The report should include (i) a description of the project, (ii) a brief overview of the design and structure chosen, (iii) the algorithms used, (iv) a list of limitations and known bugs, (v) the program listings (preferably in a flopy disk), and (v) a discussion on the potential use of the program and proposal of its future possible improvements. I will suggest in the following list some examples of projects. Survey PapersSurvey papers should be between 20 and 30 pages and should be presented in class by the author.Survey papers should summarize previous research and report on recent research issues and advances in the chosen topic. The papers should be well written and organized, and should provide a thorough summarization of the selected data mining research area. A list of references (bibliography) must be included. The evaluation of the paper would be based on the comprehensiveness and organization of the paper. Students may also opt for a research paper. A research paper should present a new idea or method to solve a given data mining problem. The approach presented should be a novel and original contribution. A research paper could be good start for a Masters or Ph.D. research. Here are some research survey topic examples:
 written by a grad student in summer 1995.
Important Dates
Projects (or survey papers) will count 35% of the overall grade. |
attempt to implement an algorithm for one of the
problems discussed in the class or from one research paper related to
data mining and knowledge discovery from large databases, or
| Readings | |||||||
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Students will have to read recent or classical research papers on data
mining or related fields, and present the papers in class. The papers
will be selected from conference proceedings such as SIGMOD,
SIGKDD, VLDB, ICDE, etc, or journals and books. A list of suggested
papers will be put on-line soon.
After each student presentation, attending students should fill in the presentation evaluation form and are asked to submit a written evaluation to the instructor (zaiane@cs.ualberta.ca) answering the following questions:
The evaluations are anonymous in the sense that only the instructor will
see your name on your evaluation.
Important Dates
Readings and class presentations will count 25% of the overall grade. |
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| Last updated: September 9th, 1999 | [About this site and list of symbols] |
