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Data Warehouse and Mining
CSIT 473
Instructor:
Dr. Natalie Nazarenko
Office:
Fenton Hall 2137
Hours:
8:00 - 9:30 am; 4:00 – 5:00 pm MWF, and by appointment
Phone:
(716) 673-4684
E-mail:
[email protected]
Text:
Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber,
Morgan Kaufmann
Description:
As an introductory course on data mining, this course introduces the
concepts, algorithms, techniques, and systems of data warehousing and
data mining, including (1) data preprocessing, (2) design and
implementation of data warehouse and OLAP systems, (3) systems,
architectures and algorithms for effective and scalable data mining,
including frequent pattern and correlation analysis, classification and
cluster analysis. The course will serve mainly senior-level computer
science undergraduate students and the first-year graduate students
interested in the field. Also, the course may attract students from other
disciplines who need to implement and/or use data warehouse and data
mining systems to analyze large amounts of data. This course will draw
materials mainly from the textbook. Students will study the materials
and complete all the course requirements.
Prerequisites: CSIT351.
Web Sites:
https://fredonia.sln.suny.edu/default.asp
www.cs.fredonia.edu/nazarenko
Attendance: You are expected to attend all lectures. Exams must be taken on the date
assigned unless prior arrangements have been made.
Make-up exams will not be given except in fully documented cases of medical or other
emergencies. Please make sure that you can attend all the exams
Grading:
1)
Homework
40%
2)
Tests, Quizzes
40%
3)
Final test
20%
Grading Scale:
94%100%
A
90%93%
A-
88%89%
B+
82%87%
B
80%81%
B-
78%79%
C+
72%77%
C
70%71%
C-
68%69%
D+
62%67%
D
Newsgroup:




Announcements will be posted to the newsgroup and our Web site. Make sure to
check the newsgroup frequently enough to stay informed.
Use the class newsgroup for your questions. As everyone can see them, it is more
likely to get quick response.
You are encouraged to answer or participate in the discussions for questions
posted on the newsgroup.
However, there are obviously things that are not appropriate for the newsgroup,
such as solutions for assignments as well as comments or requests to the staff.
Assignments/Projects: All assignments/projects are due at the beginning of class on the
dates to be set by the instructor. A 10% penalty will be assessed for each calendar day of
lateness. Assignments/projects handed in more than one week (seven days) late will not
be accepted for credit. These assignments and projects should be done with your own
efforts. All parties involved in copying a given assignment shall get grade divided by
number of participants. You should spend at least three hours per week outside of class to
complete the assignments.
Academic Honesty:
The specific actions in response to incidents of student dishonesty
are inclusive of receiving a failing grade for an exam, quiz,
assignment or the course, suspension or dismissal from college. I
grades are given only if illness, hardship or any other extra
60%61%
D-
0%59%
Fail
ordinary circumstances preclude the completion of the course. An
I grade must be arranged before the final examination.
Examination, quiz dates and assignment due dates will be
announced in class. This syllabus may be subject to change.