The module aims to introduce the fundamental and advanced issues of data and web mining, to provide students with a broad understanding of the algorithms developed to address different data and web mining tasks and apply them to real-world problems.
Prerequisite(s)
None
Corequisite(s)
None
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)
Assist. Prof. Dr. Bahar İLGEN
Course Assistant(s)
TBA
Schedule
Thursday, 17:00, 2B-11/13, Atakoy Campus
Office Hour(s)
Asst. Prof. Bahar İLGEN, Monday 14:00-15:00, 2B-02
Teaching Methods and Techniques
- Presentation, laboratory
Principle Sources
- Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
- Tan, P. N. (2006). Introduction to data mining. Pearson Education India.
- LIU, B., 2010. Web Data Mining, 2nd edition.
Other Sources
- Chakrabarti, S., 2002. Mining the Web: Analysis of Hypertext and Semi Structured Data, MorganKaufmann Publishers.
- Baldi, P., Smyth, F. P., 2003.Modeling the Internet and the Web: Probabilistic Methods and Algorithms, Wiley.
Course Schedules
Week
Contents
Learning Methods
1. Week
Overview of the course (teaching methods, sources, outcomes) Introduction and Motivation to Data Mining
Oral and written presentation.
2. Week
Data Preprocessing Techniques
Oral and written presentation.
3. Week
Classification
Oral and written presentation.
4. Week
Clustering
Oral and written presentation.
5. Week
Outlier Detection
Oral and written presentation.
6. Week
Association Rules
Oral and written presentation.
7. Week
Text Mining
Oral and written presentation.
8. Week
Social Graphs
Oral and written presentation.
9. Week
Web Mining; Web graphs and Internet
Oral and written presentation.
10. Week
Web Structure and Information retrieval
Oral and written presentation.
11. Week
Web Content Mining
Oral and written presentation.
12. Week
Analyzing and Modelling User Behaviours
Oral and written presentation.
13. Week
Recommendation Models
Oral and written presentation.
14. Week
Project Presentations
Presentation
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
30
Project(s)
1
20
Presentations
1
10
Final Exam
1
40
Program Outcomes
PO-1
an ability to apply knowledge from undergraduate and graduate engineering and other disciplines to identify, formulate, and solve novel and complex electrical/computer engineering problems that require advanced knowledge within the field
PO-2
knowledge of advanced topics within at least two subdisciplines of computer engineering
PO-3
the ability to understand and integrate new knowledge within the field;
PO-4
the ability to apply advanced technical knowledge in multiple contexts
PO-5
a recognition of the need for, and an ability to engage in, life-long learning
PO-6
the ability to plan and conduct an organized and systematic study on a significant topic within the field
PO-7
an ability to convey technical material through formal written reports which satisfy accepted standards for writing style
PO-8
the ability to analyze and use existing literature
PO-9
the ability to demonstrate effective oral communication skills
PO-10
the ability to stay abreast of advancements in the area of computer engineering
Learning Outcomes
LO-1
Knows and applies data preprocssing techniques.
LO-2
Knows basic and advanced classification algorithms and explains supervised, unsupervised and semi-supervised methods.
LO-3
Knows and applies clustering methods and algorithms.
LO-4
Knows and applies association rules and algorithms such as Apriori.
LO-5
Knows web mining topics such as content analysis, structural analysis and information retrieval, implements the algorithms to address real world problems.
LO-6
Knows and applies the analysis and modelling of user behaviours and recommendation systems.
