Course
hashtag #501445DM
MidTerm Exam
Marks
Word Cloud of Data Mining Course, Created by Dr.
Alzahrani
Data mining (the advanced analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science,is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. (See Wikipedia)
~Course
Information:
Course syllabus 
student version
Code

5014453

Title

Data Mining

Credit Hrs

30:3

Semester

2

Academic Year

20122013

Pre/Co Requisites

2022253 Probability and Statistics, 5013473 Design And Analysis Of
Algorithm

Instructor

Office Hours/ Location

Email

Teaching Assistants

Dr. Salha M. Alzahrani

Mon. 8am12pm Tues. 8am12pm Garwa deanship block, Room 1630

admin@c2learn.com
s.zahrani@tu.edu.sa
Please send your enquiries to one email address not
both!

 
Course Format

Lecture Time/Venue

Course Website

Lectures: 28/Section
Lab: 28/Section


www.c2learn.com/data_mining/

~Synopsis:
This course will introduce the students to the basic
concepts of data mining and examine methods that have emerged from both
fields of statistics and artificial intelligence. The course will survey
data mining applications, techniques and models proven to be of value in
recognizing patterns and making predictions from a domain perspective.
Topics include decision trees, classification, association, partitioning,
clustering, and text mining. The course will provide handson
experimentation of data mining algorithms using easytouse software and
online repositories.
~Course
Objectives:
CO1 : Introduce the basic concepts and techniques of
data mining. CO2 : Develop skills
of using recent data mining software for solving practical problems.
CO3 : Gain experience of doing independent study and
research.
~Learning
Outcomes:
Upon successful completion of the course,
the students will be able to:
CLO1 : Describe the basic concepts and techniques of
data mining . CLO2 : Perform
the basic data processing in data mining.
CLO3 : Understand the naïve Bayes and nearest
neighbour classification algorithms.
CLO4 : Understand and describe the use of decision
trees for classification.
CLO5 : Understand and use the association rules for data mining problems.
CLO6 : Understand and differentiate between the
hierarchical clustering and kmeans clustering.
CLO7 : Understand the basic techniques behind text
mining. CLO8 : Identify
appropriate data mining technique in solving a domain’s problem.
CLO9 : Identify the strengths and limitations of
popular data mining techniques, and promising business applications of data
mining. CLO10 : Work
independently and in a team to write and accomplish a concise research
review paper for a selected application of data mining.
~Course
Schedule:
~Course
Assessment:
Quizzes: 5, Assignment(s)/Project(s): 20, Lab: 15,
Mid Term Exam: 20, Final Exam 40.
~Text/References:
Main Textbook: Bramer, M., Principles
of Data Mining. SpringerVerlag. 2007. ISBN 9781846287657
Other Reference(s): Han, J. and
Kamber, M., Data Mining: Concepts and Techniques, 2nd Edition, Morgan
Kaufmann, 2006.
Cios, K.J., Pedrycz, W., Swiniarski, R.W., Kurgan, L.A., Data Mining: A
Knowledge Discovery Approach, Springer US, 2007.
Ian H. Witten
and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques
(Second Edition), Morgan Kaufmann, 2005.
~Course
Policies:
Attendance

it is compulsory for students to attend at least 75% of the whole
time allocated to the course. More than four (4) unjustified
absences will make you ineligible for the course.

Quizzes

students are informed about the date of their quizzes. Check the
above course schedule to see when a quiz is due.

Assignments

students are expected to submit their assignments in a timely
manner.

Late Submissions

late submission may be subjected to a decrease in the grade that is
proportional to the delayed days.

Midterms and Finals

exams are closed books. Students who did not attend the midterm due
to exceptional situations (e.g., medical emergency) will see their
midterm result calculated from their final. 0.75 * Final exam mark
will be used as their midterm result.

Plagiarism

student caught cheating will be awarded a 0 in the exam and may be
subject to university disciplinary actions.

Disability

students with disability need to approach the course instructor so
that commodities will be put in place to assist them. This is done
in a personal and confidential manner.

