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Information
| Code | CEN481 |
| Name | Introduction to Data Mining |
| Term | 2024-2025 Academic Year |
| Semester | 7. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Öğr. Gör.Dr. HAVVA ESİN ÜNAL |
| Course Instructor |
Prof. Dr. SELMA AYŞE ÖZEL
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The objective of this course is to introduce basic data mining algorithms.
Course Content
Introduction to data mining, data preprocessing, association rules, classification, clustering, outlier detection algorithms and their applications.
Course Precondition
None
Resources
Jiawei Han, Micheline Kamber and Jian Pei , Data Mining: Concepts and Techniques, 3rd edition, Morgan Koufmann,2011
Notes
Any reference to Weka and Python
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Comprehends the basic data mining algorithms. |
| LO02 | Applies data preprocessing, association rules, classification, clustering, outlier detection algortihms |
| LO03 | Applies data mining to solve up-to-date problems. |
| LO04 | Decides which data mining technique is to be applied in which case. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Adequate knowledge of mathematics, science and related engineering disciplines; ability to use theoretical and applied knowledge in these fields in solving complex engineering problems. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Ability to identify, formulate and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics. | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Ability to work effectively in interdisciplinary and multidisciplinary teams; individual working skills. | 3 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Ability to communicate effectively verbally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and understandable instructions. | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology, and constantly renew oneself. | 3 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Knowledge of ethical principles, professional and ethical responsibility, and standards used in engineering practice. | 3 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Knowledge of business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; knowledge of sustainable development. | |
| PLO11 | Bilgi - Kuramsal, Olgusal | Knowledge of the effects of engineering practices on health, environment and safety in universal and social dimensions and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Definition of data mining, and steps of data mining process | Reading the lecture notes | |
| 2 | Data preprocessing steps | Reading the lecture notes | |
| 3 | Weka package | Reading the lecture notes and making practice | |
| 4 | Association rule mining algorithms | Reading the lecture notes | |
| 5 | Performance improvements of association rule mining algortihms | Reading the lecture notes | |
| 6 | Basic classification algorithms (decision tree, Naive Bayes) | Reading the lecture notes | |
| 7 | Classifiers' performance evaluation techniques | Reading the lecture notes | |
| 8 | Mid-Term Exam | Reading the lecture notes | |
| 9 | Rule based classifiers, SVM and other classifiers | Reading the lecture notes and making practice | |
| 10 | Basic clustering algorithms (k means) | Reading the lecture notes | |
| 11 | Basic clustering algorithms (hierarchical methods) | Reading the lecture notes | |
| 12 | Outlier detection methods | Reading the lecture notes | |
| 13 | Introduction to Web and text mining | Reading the lecture notes | |
| 14 | Preparing the project presentations | Making practice, and preparing presentation | |
| 15 | Project presentations | Making practice, and preparing presentation | |
| 16 | Preparation to Final Exam | Reading the lecture notes | |
| 17 | Term Exams | Reading the lecture notes |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||