MAT310 – Data Mining

Module designation

Data Mining (MAT310)

Semester(s) in which the module is taught

6th

Person responsible for the module

Auli Damayanti, M.Si.

Language

Indonesian

Relation to curriculum

Compulsory / elective / specialisation

Teaching methods

Lecture and lesson.

Workload (incl. contact hours, self-study hours)

3×170 minutes (3×50 minutes lecture and lesson, 3×60 minutes structural activities, 3×60 minutes self-study) per week for 16 weeks

Credit points

3 CP (4,8 ECTS)

Required and recommended prerequisites for joining the module

Algorithms and Programming (MAT101)

Module objectives/intended learning outcomes

General Competence (Knowledge)

1.       Explain the concept of data mining

2.       Implement data mining methods to solve real problems

Specific Competence: Student are able to

1.       Explain the basic concepts of data mining.

2.       Explain and execute preprocessing data

3.       Explain and apply the classification method to real problems.

4.       Explain and apply cluster analysis to existing problems.

5.       Explain and determine rules associations about real problems

6.      Apply data mining algorithms to real problems

Content

Basic concepts of data mining, data preprocessing, classification methods, cluster analysis, associations, data mining applications

Examination forms

Essay test (quiz, midterm ) and grup presentation (final exam)

Study and examination requirements

Students are considered to pass if they at least have got a final score 40 (D).

Final score is calculated as follow: 10%soffskill + 12% assignment + 13% Quiz + 15% midterm + 50% final exam .

Final index is defined as follow:

Reading list

1.      Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, 3rd Edition, Morgan Kaufmann Publisher, 2012.

2.      Pang Ning Tan, Michael Steinbach, dan Vipin Kumar, Introduction to Data Mining, Addison Wesley, 2006.

3.      Dan A. Simovichi dan Chabane Djeraba, Mathematical Tools for Data Mining, Springer, 2008