Advanced Data Mining (CMPE566) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advanced Data Mining CMPE566 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Computer Engineering Elective Courses
Course Level Ph.D.
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
Supportive Courses X
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 To become familiar with the state-of-the art and the literature in the software engineering research domain X
2 An ability to conduct world-class research in software engineering and publish scholarly articles in top conferences and journals in the area
3 Be able to conduct quantitative and qualitative studies in software engineering X
4 Acquire skills needed to bridge software engineering academia and industry and to develop and apply scientific software engineering approaches to solve real-world problems X
5 An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain.
6 An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering.
7 Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies.
8 An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions. X
9 Promote the development, adoption and sustained use of standards of excellence for software engineering practices.

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