TÜRKÇE
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Information
| Code | CEN481 |
| Name | Introduction to Data Mining |
| Term | 2023-2024 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 | Has capability in the fields of mathematics, science and computer that form the foundations of engineering | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Identifies, formulates, and solves engineering problems, selects and applies appropriate analytical methods and modeling techniques, | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Analyzes a system, its component, or process and designs under realistic constraints to meet the desired requirements,gains the ability to apply the methods of modern design accordingly. | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Ability to use modern techniques and tools necessary for engineering practice and information technologies effectively. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to design and to conduct experiments, to collect data, to analyze and to interpret results | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Has ability to work effectively as an individual and in multi-disciplinary teams, take sresponsibility and builds self-confidence | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Can access information,gains the ability to do resource research and uses information resources | 4 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Awareness of the requirement of lifelong learning, to follow developments in science and technology and continuous self-renewal ability | 3 |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to communicate effectively orally and in writing, and to read and understand technical publications in at least one foreign language | 3 |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Professional and ethical responsibility, | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Awareness about project management, workplace practices, employee health, environmental and occupational safety, and the legal implications of engineering applications, | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Becomes aware of universal and social effects of engineering solutions and applications, entrepreneurship and innovation, and knowledge of contemporary issues |
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 | ||