CENG394 Data MiningIstanbul Okan UniversityDegree Programs Computer Engineering (English)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Computer Engineering (English)
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code: CENG394
Ders İsmi: Data Mining
Ders Yarıyılı: Fall
Ders Kredileri:
Theoretical Practical Credit ECTS
3 0 3 7
Language of instruction: EN
Ders Koşulu:
Ders İş Deneyimini Gerektiriyor mu?: No
Type of course: Compulsory
Course Level:
Bachelor TR-NQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Assoc. Prof. PINAR YILDIRIM
Course Lecturer(s): Assoc. Prof. PINAR YILDIRIM
Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives: The purpose of the course is to educate students about the main concepts and methods of data mining.
Course Content: The course contains these topics: classification, clustering, association algorithms and data mining studies in different areas.

Learning Outcomes

The students who have succeeded in this course;
Learning Outcomes
1 - Knowledge
Theoretical - Conceptual
1) Ability to explain the concepts of data mining
2) Ability to apply data preprocessing techniques
3) Ability to use the algorithms of data mining.
4) Ability to use the tools of data mining
5) Ability to produce solutions in the subjects of data mining
2 - Skills
Cognitive - Practical
3 - Competences
Competence to Work Independently and Take Responsibility
Field Specific Competence
Learning Competence
Communication and Social Competence

Ders Akış Planı

Week Subject Related Preparation
1) • What Motivated Data Mining? Why Is It Important? • So, What Is Data Mining? • Data Mining—On What Kind of Data? • Data Mining Functionalities—What Kinds of Patterns Can Be Mined? Reading chapter 1
2) • What is data? • Attributes. • Types of attributes. • Discrete and continuous variables. • Types of data set. • Record data. • Data matrix. • Document data. • Transaction data. • Graph data. • Chemical data. • Ordered data. • Why data preprocessing? • Why is data dirty? • Why is data preprocessing important? • Multi-dimensional measure of data quality. • Major tasks in data preprocessing. • Data quality. • Noise. • Outliers. Reading chapter 2.
3) • Missing values. • Duplicate data. • Mining data descriptive characteristics. • Measuring the central tendency. • Symmetric vs. skewed data. • Properties of normal distribution curve. • Histogram analysis. • Positively and negatively correlated data. • Not correlated data. • Data cleaning. • How to handle missing data? • How to handle noisy data? • Simple discretization methods: Binning. • Regression. • Cluster analysis. • Data cleaning as a process. • Aggregation. • Sampling. • Types of sampling. • Sample size. Reading chapter 2.
4) • Classification. • Illustrating classification task. • Examples of classification task. • Classification techniques. • Example of a decision tree. • Another example of decision tree. • Apply model to test data. • Decision tree induction. • Issues: data preparation. • Issues: evaluating classification methods. • Algorithm for decision tree induction(ID3/C4.3). • Attribute selection measure: Information gain. • Decision tree example. Reading chapter 8.
5) • Numeric variables and missing values. • Overfitting and tree pruning. • Enhancements to basic decision tree induction. • Model evaluation. • Metrics for performance evaluation. • Limitation of accuracy. • Cost matrix. • Calculation of accuracy. • Cost-sensitive measures. • Model evaluation. • Methods for performance evaluation. • Methods of estimation. • ROC (Receiver Operating Characteristic). • Instance Based Classification. • Nearest neighbor classification. • k-Nearest neighbor algorithm example. Reading chapter 8-9.
6) • What is cluster analysis? • Applications of cluster analysis • What is not cluster analysis? • Notion of a cluster can be ambiguous. • Types of clustering. • Characteristics of the input data are important. • Clustering algorithms. • Hierarchical clustering. • Agglomerative clustering algorithm. • Cluster distance measures. • Single link(min) hierarchical clustering. • Single link(min) hierarchical clustering example. Reading chapter 10.
7) • Complete link(max) hierarchical clustering example. • K-means clustering. • Importance of choosing initial centroids. • Limitations of k-means. • Overcoming k-means limitations. • K-means clustering example. Reading chapter 10.
8) Midterm1
9) • Association rule mining • Frequent itemset • Association rule • Association rule mining task • • Apriori algorithm • Apriori algorithm example Reading chapter 6.
10) • Statistical classification models. • Bayes theorem and classifier. • Bayes classifier example. • Continuous variables. Reading chapter 6.
11) • Text and web mining. • Natural language processing. • Part-of-speech tagging. • Word sense disambiguation. • Text databases and IR. • Indexing techniques. • Types of text data mining. • Text classification. • Document clustering. • Text categorization. • Categorization methods. • Vector space model.
12) Midterm2
13) Project presentations
14) Project presentations
15) Final exam

Sources

Course Notes / Textbooks: Data Mining Concept and Techniques
J.Han and M.Kamber
@2012| Morgan Kaufmann Publishers
ISBN 978-0-12-381479-1
References: Yok / None

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları

1

2

3

4

5

Program Outcomes
1) Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2) The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
3) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5) Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems.
6) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7) Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge.
8) Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Professional and ethical responsibility.
10) Information on project management and practices in business life such as risk management and change management; awareness about entrepreneurship, innovation and sustainable development.
11) Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions.

Ders - Öğrenme Kazanımı İlişkisi

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2) The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. 2
3) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively. 3
5) Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems.
6) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill. 5
7) Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge.
8) Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Professional and ethical responsibility.
10) Information on project management and practices in business life such as risk management and change management; awareness about entrepreneurship, innovation and sustainable development. 3
11) Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions.

Öğrenme Etkinliği ve Öğretme Yöntemleri

Course
Proje Hazırlama

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Bireysel Proje

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Project 1 % 10
Midterms 2 % 50
Final 1 % 40
total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
total % 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities Number of Activities Workload
Course Hours 16 48
Project 2 40
Midterms 12 100
Final 4 32
Total Workload 220