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DICLE UNIVERSITY
SCIENCE INSTITUTE
Department of Mathematics
COURSE INFORMATION PACKAGE
Course Code
Optic Code
Consultation Hours
T+A
Credit
ECTS
504045
10504045
To be announced
3+0
3
8
Course Title
DATA MINING
Year/Semester
- / FALL
Status
SELECTIVE
Programme’s Name
MASTER
Language of instruction
TURKISH
Prerequisites
NO
Disabled Students
In case of need, Handicapped students can request some facilities by giving some information
about themselves.
Student Responsibilities
In terms of the content of course;to get ready, to participate, and to carry out responsibilities,
which are homework, project, disputation, and reading the interested parts about the course to be
performed
Lecturer
Assistant Prof..Abdullah BAYKAL, e-mail:[email protected],
Course Assistant
NO
Course Objectives
Learning Data Mining programming
Special Quota for
Other Departments
The most 10 (ten) student
Tel:3221
At the end of this course students will learn;
- How data mining is emerged and how it can use in which situation he understands.
- Techniques used in data mining and the techniques used in the statistical methods
to understand their relationship.
- Data mining will have information about the importance of Preprocessing
process
- Data conversion and merge operations is to understand the requirements of
- Data mining models will have information about the differences between
Learning Outcomes
- Learning to learn how to create and test sets
- Decision trees can use in which situation will decide
- K-nearest neighbour method with other methods understands the difference
- Neural networks
- Process models
- The use of the programs used in data mining.
504045
10504045
DATA MINING
3+0
3
8
Contents, learning activities
Week
Topic
Learning Activities
1
What is Data Mining ?
Introduction of data mining and discussion
2
Data Mining Methodology
Questions and answers on the methods used
3
Data mining and OLAP
Information about its usage area
4
Data mining applications
Debate about the applications made
5
Data Mining Preprocessing
The concept of data, data use, what can the data
be and answer questions
6
Data cleaning / integration
Why data can be noisy ,How it can be
removed,debate
7
Midterm Exams
Debate about exam questions
8
Data transfornations / reduction
What are the benefits of data conversion,debate
9
Data Mining Models and Algorithms
The model selection that should be used
10
K-nearest neighbor and memory-based reasoning (MBR)
Method of “K nearest neighbour”
11
Neural networks
Neural trees presentation, use the method
12
Decision trees
Creating a decision tree, classification
13
Discriminant analysis
Analysis using
14
Logistic regression
Regression
15
Used Programs
The use of WEKA program
If any, mark as x
Midterm Exams
Percent (%)
X
20
X
20
X
60
Quizzes
Homeworks / Term Paper / Presentation
Assessment criteria
Projects
Attendance & cover a subject
Others (in training, field survey, thesis
preparation vb).
Final Exam
Textbook
Recommended
Reading
Regulating
1.
Others
Will be given
points to
determine his
marks of this
course in certain
percentages
with respect to
activities during
the process have
been realized by
student in the
class
-Data Mining Concepts and Techniques, Jiawei Han and Micheline Kamber
- abdullahbaykal.t35.com
Discipline of Applied Mathematics in Mathematics
2.
3.
4.
5.
Efficiency examples: Contribution to course, homework activities, seminars, study in laboratory, scanning on paper and books,
observation, contribution to activities, sample study on case, etc.
Course’s time is determined according to examination, quiz, homework, project, and contribution to class.
Average mark about course is determined by above activities and booked down student information system of
university.
Midterm exam will be planned between 7 and 10’th week of semester by related lecturer.
ECTS calculation form will contain checkout of course.
6.
Checkout course paper will be given to students at beginning of each semester.