Advanced Data Mining (CMPE566) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advanced Data Mining CMPE566 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Elective Courses
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives To develop an understanding of basic data mining concepts ,the strengths and limitations of popular data mining techniques, and to be able to identify promising business applications of data mining.
Course Learning Outcomes The students who succeeded in this course;
  • Understand the basic concepts and techniques of Data Mining.
  • Create a clean, consistent repository of data within a data warehouse
  • Actively manage in data mining projects.
  • develop skills of using recent data mining software for solving practical problems
Course Content Introduction to data mining, concepts, attributes and instance, data processing (cleaning, integration and reduction), data warehousing and online analytical processing (OLAP), data mining algorithms, credibility, advanced pattern mining, classification, engineering the input and output, data mining software and applications.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to Data Mining Lecture Notes Chapter 1 (Text Book 1)
2 Input: Concepts, attributes and instance Lecture Notes Chapter 2 (Text Book 2)
3 Data Processing (Cleaning, Integration and Reduction) Lecture Notes Chapter 3 (Text Book 1)
4 Data Warehousing and Online Analytical Processing (OLAP) Lecture Notes Chapter 4 (Text Book 1)
5 Data Mining Algorithms: Basic Methods Lecture Notes Chapter 4 (Text Book 2)
6 Credibility: Evaluating what’s been learned Lecture Notes Chapter 5 (Text Book 2)
7 Credibility: Evaluating what’s been learned Lecture Notes Chapter 5 (Text Book 2)
8 Advanced Pattern Mining Lecture Notes Chapter 7 (Text Book 1)
9 Advanced Pattern Mining Lecture Notes Chapter 7 (Text Book 1)
10 Classification: Basic Concepts Lecture Notes Chapter 8 (Text Book 1)
11 Classification: Basic Concepts Lecture Notes Chapter 8 (Text Book 1)
12 Transformations: Engineering the Input and Output Lecture Notes Chapter 7 (Text Book 2)
13 Transformations: Engineering the Input and Output Lecture Notes Chapter 7 (Text Book 2)
14 Advanced techniques, Data Mining software and applications Lecture Notes Chapter 12 (Text Book 2)
15 Review
16 Review

Sources

Course Book 1. Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2006.
2. Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, 2005.
3. Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Introduction to Data Mining. Addison Wesley, 2005.
Other Sources 4. Tom Mitchell. Machine Learning. McGraw Hill, 1997.
5. R. O. Duda et al., Pattern Classification. Wiley Interscience
6. Hastie, Tibshirani and Friedman. The Elements of Statistical Learning. Springer-Verlag, 2001.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 3 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 35
Final Exam/Final Jury 1 35
Toplam 5 100
Percentage of Semester Work 65
Percentage of Final Work 35
Total 100

Course Category

Core Courses
Major Area Courses X
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply knowledge of mathematics, science, and engineering. X
2 An ability to design and conduct experiments, as well as to analyse and interpret data. X
3 An ability to design a system, component, or process to meet desired needs. X
4 An ability to function on multi-disciplinary domains. X
5 An ability to identify, formulate, and solve engineering problems. X
6 An understanding of professional and ethical responsibility. X
7 An ability to communicate effectively. X
8 Recognition of the need for, and an ability to engage in life-long learning. X
9 A knowledge of contemporary issues. X
10 An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. X
11 Skills in project management and recognition of international standards and methodologies X
12 An ability to produce engineering products or prototypes that solve real-life problems. X
13 Skills that contribute to professional knowledge. X
14 An ability to make methodological scientific research. X
15 An ability to produce, report and present an original or known scientific body of knowledge. X
16 An ability to defend an originally produced idea. X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project 3 5 15
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 10 10
Prepration of Final Exams/Final Jury 1 20 20
Total Workload 77