DATA MINING

AIM

To introduce students to and get them involved in basic data mining techniques and applications.

CONTENT

This course contains; Introduction to Data Mining,Data Mining Process,Data Discovery and Visualization,Feature Selection and Data Transformation ,Clustering Methods,Exercise: Clustering,Classification Methods - Decision Trees ,Exercise: Classifications,Exercise: Classifications,Association Rule Mining,Exercise: Association rule mining,Exercise: Problem oriented data mining,Midterm project presentations,Midterm project presentations .

1. 1

   Understands the basic concepts and processes related to data mining.

   Taught by: Question - Answer Technique, Micro Teaching Technique, Lecture Method · Assessed by: Traditional Written Exam

2. 2

   Define data mining and its objectives.

   Taught by: Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam

3. 3

   Understands data, database, data warehouse and related basic concepts.

   Taught by: Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam

4. 4

   Describe the data mining process.

   Taught by: Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam

5. 5

   Describes common data mining methods.

   Taught by: Case Study Method, Self Study Method, Question - Answer Technique, Micro Teaching Technique, Experiential Learning, Flipped Classroom Learning, Lecture Method · Assessed by: Traditional Written Exam

6. 6

   Understands the clustering method.

   Taught by: Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam

7. 7

   Defines classification methods.

   Taught by: Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam

8. 8

   Distinguishes which data mining method to use in a given problem.

   Taught by: Discussion Method, Case Study Method, Question - Answer Technique, Inquiry-Based Learning, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam

9. 9

   Distinguish the differences between descriptive and predictive methods.

   Taught by: Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam

10. 10

    Propose a correct (supervised/unsupervised) method for a given data mining problem.

    Taught by: Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Quiz

11. 11

    Interpret the most possible methods for a given data set with respect to the data types and pattern.

    Taught by: Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam

12. 12

    Defines clinical and management decision support systems and their types.

    Taught by: Self Study Method, Micro Teaching Technique, Inquiry-Based Learning, Cooperative Learning, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam

13. 13

    Defines knowledge-based DSS.

    Taught by: Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam

14. 14

    Defines supervised KDs.

    Taught by: Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam

15. 15

    Defines the concept and processes of data mining.

    Taught by: Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam

16. 16

    It uses a data mining tool (KNIME).

    Taught by: Self Study Method, Question - Answer Technique, Project Based Learning Model, Inquiry-Based Learning, Cooperative Learning, Experiential Learning, Flipped Classroom Learning, Lecture Method · Assessed by: Homework, Project Task

17. 17

    Apply the data preprocess method with KNIME.

    Taught by: Problem Solving Method, Question - Answer Technique, Micro Teaching Technique, Flipped Classroom Learning, Lecture Method · Assessed by: Homework, Project Task

18. 18

    Use the clustering methods with KNIME.

    Taught by: Question - Answer Technique, Inquiry-Based Learning, Flipped Classroom Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

19. 19

    Use the classification methods with KNIME.

    Taught by: Question - Answer Technique, Flipped Classroom Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

20. 20

    Use the association rules with KNIME.

    Taught by: Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam

WEEKLY PLAN

1. WEEK 1

   Introduction to Data Mining

   Preparation: Basic database concepts

2. WEEK 2

   Data Mining Process

   Preparation: The usage of SQL as DML; TSQL; data warehouse architectures, the reasons yield data manipulation

3. WEEK 3

   Data Discovery and Visualization

   Preparation: The graph types in data visualization and the components of a graph, such as dimension, measure, etc.

4. WEEK 4

   Feature Selection and Data Transformation

   Preparation: Data types, generalization, specialization of data

5. WEEK 5

   Clustering Methods

   Preparation: Main clustering approaches, such as Hierachical Clustering, Centroid-based Clustering, Density-based Clustering, Distribution-based Clustering.

6. WEEK 6

   Exercise: Clustering

   Preparation: The excercises with KNIME for especially clustering

7. WEEK 7

   Classification Methods - Decision Trees

   Preparation: The main differences between clustering and clalssifications, how to generate a decision tree and the main decision tree algorithms, such as ID 3 and C4.5

8. WEEK 8

   Exercise: Classifications

   Preparation: The excercises with KNIME for especially classification

9. WEEK 9

   Exercise: Classifications

   Preparation: The excercises with KNIME for especially classification

10. WEEK 10

    Association Rule Mining

    Preparation: Market box analysis

11. WEEK 11

    Exercise: Association rule mining

    Preparation: The excercises with KNIME for especially ARM

12. WEEK 12

    Exercise: Problem oriented data mining

    Preparation: Clustering, decision trees, association rule mining

13. WEEK 13

    Midterm project presentations

    Preparation: A data mining solution having feature selection, data transformation, data mining application and evaluation of the result

14. WEEK 14

    Midterm project presentations

    Preparation: A data mining solution having feature selection, data transformation, data mining application and evaluation of the result

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• Data Mining: Concepts and Techniques, Morgan Kaufmann Publisher San Francisco - Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley, (2005)

TEACHING STAFF

• Prof.Dr. Reda ALHAJJCOORDINATOR
• Prof.Dr. Reda ALHAJJ