COURSE DESCRIPTION:
This course provides an introduction to Knowledge
Discovery in Databases (KDD) and Data Mining. KDD deals
with data integration techniques and with the discovery,
interpretation and visualization of patterns in large
collections of data. Topics covered in this course
include data warehousing and mediation techniques; data
mining methods such as rule-based learning, decision
trees, association rules and sequence mining; and data
visualization. The work discussed originates in the
fields of artificial intelligence, machine learning,
statistical data analysis, data visualization, databases,
and information retrieval. Several scientific and
industrial applications of KDD will be studied.
RECOMMENDED BACKGROUND:
CS4341 Introduction to Artificial Intelligence, MA2611 Applied
Statistics I, and CS3431 Database Systems I.
CLASS MEETING:
Tuesdays and Fridays 11:00-12:50 pm
Room: WB323
Please come to class on time and stay for the whole class period.
COURSE OUTCOMES:
- Learn and use computational techniques for data transformation, integration, and cleaning.
Practice with and evaluation of this outcome:
Projects 0, 1, 2, 3, 4. Exams 1, 2
- Learn and use computational techniques for discovering patterns and trends in data collections.
Practice with and evaluation of this outcome:
Projects 1, 2, 3, 4. Exams 1, 2
- Learn and use computational approaches for constructing and evaluating predictive models and descriptive models built upon patterns discovered from data.
Practice with and evaluation of this outcome:
Projects 1, 2, 3, 4. Exams 1, 2
- Apply course material to discover patterns from data in a variety of application domains.
Practice with and evaluation of this outcome:
Projects 1, 2, 3, 4.
- Analyze and experimentally evaluate algorithms and implementations of data mining techniques in multiple real-world application domains.
Practice with and evaluation of this outcome:
Projects 1, 2, 3, 4.
PROFESSOR:
Prof. Carolina Ruiz
Office: FL 232
Phone Number: (508) 831-5640
Office Hours:
| Mondays | 1:00 pm | - | 2:00 pm
|
| Thursdays | 12:00 pm | - | 1:00 pm
|
| if the above times don't work for you, contact Prof. Ruiz to schedule a different time |
|
TEACHING ASSISTANT:
Yutao Wang
Office Hours: Fuller Labs A22
| Mondays | 3:00 pm | - | 4:00 pm
|
| Fridays | 9:00 am | - | 10:00 am
|
| if the above times don't work for you, contact Yutao to schedule a different time |
|
See Class Mailing Lists
for instructions on how to reach the professor and the TA by email.
TEXTBOOK:
Several other books on the subject and related subjects are
recommended below.
Some research papers will be handed out during the term.
GRADES:
| Exam 1 | 20%
|
| Exam 2 | 20%
|
| Project 1 | 10%
|
| Project 2 | 12%
|
| Project 3 | 12%
|
| Project 4 | 12%
|
| Project 5 | 14%
|
| Class Participation and Pop Quizzes:
| Extra Points
|
Your final grade will reflect your own work and achievements
during the course. Any type of cheating
will be reported to the WPI Judicial Board and penalized
in accordance with the
Academic Honesty Policy.
According to the
WPI Undergraduate Catalog, "Unless otherwise indicated,
WPI courses usually carry credit of 1/3 unit. This level of activity
suggests at least 17 hours of work per week, including class and laboratory
time." Hence,
you are expected to spend at least 13 hours
of work per week on this course outside the classroom.
BS/MS GRADUATE CREDIT
This course may be taken for graduate credit by students in the BS/MS CS program.
Written permission from the professor is required. In order to receive graduate
credit, students who have signed up for this program need to work on projects/homework
alone (that is, in "groups" of 1 student).
EXAMS
Format
There will be a total of 2 exams.
Each exam will cover the
material presented in class since the beginning of the term.
In particular, the final exam is cumulative.
Exams will be in-class, closed-book, individual exams.
Collaboration or other outside assistance on exams is not allowed.
Check the course schedule for exam dates.
Makeups
Regarding makeup exams,
I follow Prof. Gennert's policy:
"Makeup and/or early examinations are not given except under the most
dire of circumstances, and then only with corroborating documentation.
Note well that neither oversleeping, forgetting to show up for an exam,
nor conflicting travel arrangements are considered dire circumstances."
PROJECTS & HOMEWORK
There will be several projects assigned during the term.
Each of the projects deals with one or more of the data mining techniques
covered in the class.
Data Mining Tool
For most of the projects, we will use the
Weka system
(http://www.cs.waikato.ac.nz/ml/weka/).
Weka is an excellent machine-leaning/data-mining environment.
It provides a large collection of Java-based mining algorithms,
data preprocessing filters, and experimentation capabilities.
Weka is open source software issued under the GNU General Public License.
For more information on the Weka system, to download the system and
to get its documentation, look at
Weka's webpage
(http://www.cs.waikato.ac.nz/ml/weka/).
You should download and use the latest developer version (currently weka-3-7-2)
of the system.
Teams
Students are expected to organize themselves into groups of exactly 2
for each of the projects, except for students taking this course
for BS/MS credit who are expected to work on the projects alone.
Each project will contain both an individual assignment and a group
assignment.
Groups need not be the same for all projects.
Submissions and Late Policy
See each project statement for details.
Project Descriptions
More detailed descriptions of the projects will be posted to the course webpage
at the appropriate times during the term.
Although you may find similar programs/systems available online or in the
references,
the design and all code you use and submit, the results, and the analysis of the results
in your projects/homework submissions MUST be your own original work.
CLASS PARTICIPATION
Students are expected to read the material assigned for each
class in advance and to participate in class discussions.
Class participation will be taken into account when deciding
students' final grades.
CLASS MAILING LISTS AND myWPI
There are two mailing lists for this class (replace XXXX with 4445 below):
There is also a myWPI account for this class that will be used for project
submissions only, as needed.
CLASS WEB PAGES
The web pages for this class are located at
http://www.cs.wpi.edu/~cs4445/b10/
Announcements will be posted on the web pages and/or the class mailing
list, and so you are urged to check your email and the class web pages
frequently.
WARNING:
Small changes to this syllabus may be made during the course
of the term.
ADDITIONAL REFERENCES
Knowledge Discovery and Data Mining
-
"Data Mining: Concepts and Techniques".
J. Han and M. Kamber. Morgan Kaufmann Publishers. 2001.
ISBN: 1-55860-489-8.
-
"Advances in Knowledge Discovery and Data Mining". Eds.: Fayyad,
Piatetsky-Shapiro, Smyth, and Uthurusamy. The MIT Press, 1995.
-
"Data Mining. Technologies, Techniques, Tools, and Trends".
B. Thuraisingham. CRC, 1998.
-
"Data Mining. A hands-on approach for business professionals".
R. Groth. Prentice Hall, 1998.
-
"Data Preparation for Data Mining". Dorian Pyle, 3/99.
- "Data Mining".
P. Adriaans & D. Zantinge
-
"Data Mining Methods for
Knowledge Discovery" Cios, Pedrycz, & Swiniarski, 1998.
-
"Data Mining Techniques for
Marketing, Sales and Customer Support". Berry & Linoff.
-
"Decision Support using
Data Mining". Anand and Buchner.
-
"Feature
Selection for Knowledge Discovery and Data Mining". Liu
and Motoda.
-
"Feature Extraction, Construction and Selection:
A Data Mining Perpective". Eds: Motoda and Liu.
- "Introduction to Data Mining".
Tan, Steinbach, & Kumar. 2006.
- "The Text Mining Handbook:
Advanced Approaches in Analyzing Unstructured Data".
Ronen Feldman, James Sanger. 2006.
-
"Knowledge Acquisition from Databases". Xindong Wu.
-
"Mining Very Large Databases with
Parallel Processing".
Alex Freitas, Simon Lavington.
-
"Predictive Data-Mining: A
Practical Guide". Weiss & Indurkhya.
- "Machine Learning and Data Mining: Methods and Applications."
Michalski, Bratko, and Kubat, 1998; John Wiley & Sons.
- "Rough Sets and Data Mining: Analysis of Imprecise Data."
Eds: Lin and Cercone; Kluwer.
-
"Seven Methods for Transforming Corporate Data into
Business Intelligence". Vasant Dhar and Roger Stein; Prentice-Hall,
1997.
Machine Learning
-
"Machine Learning".
Tom M. Mitchell.
McGraw-Hill, 1997.
-
"Elements of Machine Learning".
P. Langley.
Morgan Kaufmann Publishers,
Inc. 1996.
General AI
-
"Artificial Intelligence: A Modern Approach".
S. Russell, P. Norvig.
Prentice Hall, 1995. ISBN 0-13-103805-2
-
"Artificial Intelligence: Theory and Practice".
T. Dean, J. Allen, Y. Aloimonos.
The Benjamin/Cummings Publishing Company, Inc. 1995.
-
"Readings in Artificial Intelligence".
B. L. Webber, N. J. Nilsson, eds.
Tioga Publishing Company, 1981.
-
"Artificial Intelligence".
3rd edition.
Patrick H. Winston.
Addison Wesley.
-
"The Elements of Artificial Intelligence Using Common Lisp".
S. L. Tanimoto.
Computer Science Press 1990.
-
"Artificial Intelligence" Second edition.
E. Rich and K. Knight.
McGraw Hill 1991.
-
"Paradigms of Artificial Intelligence Programming: Case Studies
in Common Lisp".
P. Norvig.
Morgan Kaufmann Publishers, 1992.
-
"Essentials of Artificial Intelligence".
M. Ginsberg.
Morgan Kaufmann Publishers,
1993.
-
"Artificial Intelligence Structures
and Strategies for Complex Problem Solving".
Third edition.
G. F. Luger and W. A. Stubblefield.
Addison-Wesley,
1998.
-
"Logical Foundations of Artificial Intelligence".
M.R. Genesereth and N. Nilsson.
Morgan Kaufmann, 1987.
Databases
Statistics
- "Statistical Inference for Management and Economics".
P. Billingsley, D. Croft, D. Huntsberger, C. Watson.
Boston: Allyn and Bacon, Inc. 1986.
- "Probability and Statistics". 2nd edition.
M. DeGroot. Addison Wesley, 1986.
- "Statistical Inference".
G. Casella, R. Berger.
Wadsworth and Brooks/Cole, 1990.
OTHER ONLINE RESOURCES:
Previous offerings of CS4445
Webpages of my previous offerings of this course have plenty of
useful resources: practice exams, exams, homework, solutions of
those exams/hw, etc.
Data Sets
KDD
KDD Commercial Products / Prototypes
Data Warehousing and OLAP
Machine Learning
Statistics
General AI
