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| Course Description |
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| Course Name |
: |
Data Mining |
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| Course Code |
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CENG-552 |
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| Course Type |
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Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Spring (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
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Asst.Prof.Dr. SELMA AYŞE ÖZEL
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| Learning Outcomes of the Course |
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Learns and applies data preprocessing techniques.
Prepares data for mining hidden relationships.
Extracts and comments on association rules.
Makes prediction using basic regression model.
Works with classification algorithms.
Works with clustering algorithms.
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| Mode of Delivery |
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Face-to-Face |
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| Prerequisites and Co-Prerequisites |
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None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
To investigate the hidden correlations and relationships within the huge amount of data. To learn data mining methods and algorithms and apply them in practice. |
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| Course Contents |
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Intoduction to the data mining concept. Data preprocessing: summarization, cleaning, integration, transformation, reduction, dizcretization, and generation of concept hierarchies. Introduction to data warehouses and OLAP technology. Supervised and unsupervised learning techniques. Algorithms for association rule mining: Apriori and FP-Growth algorithms. Classification and prediction methods: decision trees, bayes classifier, rule based classifiers, support vector machines, neural networks and other classification methods, regression analysis. Performance evaluation methods for classifiers and predictors. Clustering analysis: partitioning based methods, hierarchical methods, density based methods, grid based methods, model based methods. Clustering for high dimensional data. Outlier anaylsis. Applicaiton of data mining methods to stream, time series, multimedia, text, and Web data. |
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| Language of Instruction |
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English |
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| Work Place |
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Room |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
Intoduction to data mining concept. |
Reading the lecture notes |
Lecture, sample applications in class |
|
2 |
Data preprocessing: Descriptive data summarization, data cleaning. |
Reading the lecture notes |
Lecture, sample applications in class |
|
3 |
Data preprocessing: Data integration and transformation, data reduction. |
Reading the lecture notes, making research for the presentation topic |
Lecture, sample applications in class |
|
4 |
Data preprocessing: Data discretization, concept hierarchy generation. |
Reading the lecture notes, making research for the presentation topic |
Lecture, sample applications in |
|
5 |
Mining and commenting association rules. |
Reading the lecture notes, making research for the presentation topic |
Lecture, sample applications in class |
|
6 |
Classification algorithms: Decision trees, Bayesian classifier, Rule-based classifiers. |
Reading the lecture notes, preparing the oral presentation. |
Lecture, sample applications in class |
|
7 |
Classification algorithms: Artificial neural networks, support vector machines, associative classification, k nearest neighbor classifier. |
Reading the lecture notes, preparing the oral presentation. |
Lecture, sample applications in class |
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8 |
Prediction with linear regression, performance analysis of classifiers and predictors. |
Reading the lecture notes, implementing the Project work |
Lecture, sample applications in class |
|
9 |
Clustering analysis: Data preprocessing and distance measuments, k-means and k-medoids algorithms. |
Reading the lecture notes, implementing the Project work |
Lecture, sample applications in class |
|
10 |
Clustering analysis: hierarchical methods, density based methods, grid based methods. |
Reading the lecture notes, implementing the Project work |
Lecture, sample applications in class |
|
11 |
Clustering analysis: model based methods, constraint based methods, outlier analysis. |
Reading the lecture notes, implementing the Project work |
Lecture, sample applications in class |
|
12 |
Analysis of time series and biological data. |
Reading the lecture notes, preparing the Project report |
Student oral presentations and discussion sessions. |
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13 |
Analysis of graph, multimedia, and Web data. |
Reading the lecture notes, preparing the Project report |
Student oral presentations and discussion sessions. |
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14 |
Case studies and project presentations. |
Reading the lecture notes, preparing the Project presentation |
Student oral presentations and discussion sessions. |
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15 |
Case studies and project presentations. |
Reading the lecture notes, preparing the Project presentation |
Student oral presentations and discussion sessions. |
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16/17 |
Final Exam. |
Reading the lecture notes |
In class written exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 J. Han, M. Kamber, "Data Mining Concepts and Techniques", second edition, Morgan Kaufmann, 2006
I. H. Witten, E. Frank, M. A. Hall, "Data Mining: Practical Machine Learning Tools and Techniques", Third Edition, Morgan Kaufmann, 2011.
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| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
0 |
0 |
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Homeworks/Projects/Others |
3 |
100 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
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100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Reaches wide and deep knowledge through scientific research in the field of computer engineering, evaluates, implements, and comments. |
5 |
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2 |
Describes and uses information hidden in limited or missing data in the field of computer engineering by using scientific methods and integrates it with information from various disciplines. |
5 |
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3 |
Follows new and emerging applications of computer engineering profession, if necessary, examines and learns them |
5 |
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4 |
Develops methods and applies innovative approaches in order to formulate and solve problems in computer engineering. |
5 |
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5 |
Proposes new and/or original ideas and methods in the field of computer engineering in developing innovative solutions for designing systems, components or processes. |
5 |
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6 |
Designs and implements analytical modeling and experimental research and solves the complex situations encountered in this process in the field of Computer Engineering |
5 |
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7 |
works in multi disciplinary teams and takes a leading role and responsibility. |
4 |
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8 |
Learns at least one foreign language at the European Language Portfolio B2 level to communicate orally and written |
5 |
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9 |
Presents his/her research findings systematically and clearly in oral and written forms in national and international meetings. |
4 |
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10 |
Describes social and environmental implications of engineering practice. |
4 |
|
11 |
Considers social, scientific and ethical values in collection, interpretation and announcement of data. |
5 |
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12 |
Acquires a comprehensive knowledge about methods and tools of computer engineering and their limitations. |
5 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
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Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
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Homeworks, Projects, Others |
3 |
16 |
48 |
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Mid-term Exams (Written, Oral, etc.) |
0 |
0 |
0 |
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Final Exam |
1 |
20 |
20 |
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Total Workload: | 152 |
| Total Workload / 25 (h): | 6.08 |
| ECTS Credit: | 6 |
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