SYLLABUS
1. Data about the program
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
University name
Faculty
Department
Domain of study
Cycle of study
Program of study/Qualification
Form of education
Discipline code
Tehnical University of Cluj-Napoca
Automation and Computer Science
Computer Science
Computer Science and Information Technology
Bachelor
Computer Science/ Engineer
Full time
2. Data about the discipline
2.1 Discipline name
2.2 Subject area
2.3 Collaborators
Data Mining
Computer Science and Information Technology
Drd. Mihai Cuibus – Mihai.Cuibus@cs.utcluj.ro
Prep. ing. Camelia Lemnaru - Camelia.Lemnaru@cs.utcluj.ro
Prof. dr. ing. Rodica Potolea - Rodica.Potolea@cs.utcluj.ro
2.4 Lecturer
Prof. dr. ing. Rodica Potolea - Rodica.Potolea@cs.utcluj.ro
2.5 Year of study IV 2.6 Semester 1 2.7 Evaluation exam
2.8 Discipline regime
3. Estimated total time
IV/1
Data Mining
Weeks Course Applications Course Applications
[hours/week]
S
14
2
L
1
Ind.
Stud.
Credit
Discipline name
TOTAL
Year
/
Sem
102
4
[hours/semester]
P
S
28
L
14
3.1 Number of hours per week
3.2 of which course
3
2 3.3
3.4 Total hours in the teaching plan
3.5
of
which
course
102
28 3.6
Individual study
Manual, lecture material and notes, bibliography
Supplementary study in the library, online and in the field
Preparation for seminars/laboratory works, homework, reports, portfolios, essays
Tutoring
Examination
Other activities
3.7 Total hours individual study
60
3.8 Total hours per semester
102
3.9 Number of credits
4
P
60
applications
applications
1
14
Hours
4. Preconditions (where appropriate)
4.1
4.2
Curriculum based
Competence based
Fundamental Algorithms, Special Mathematics in Engineering
Mathematical apparatus, analysis and formalization, algorithms
and algorithms analysis, evaluation and performance
measurement
5. Conditions (where appropriate)
5.1
5.2
Of the course
Of the applications
videoprojector, PC/notebook, blackboard
PC/notebook, specific software
1
30
14
14
2
-
Cross skills
Profesional skills
6. Key skills gained
C3 - Problem solving using specific Computer Science and Computer Engineering tools
 C3.1 - Identifying classes of problems and solving methods that are specific to computing
systems
 C3.2 - Using interdisciplinary knowledge, solution patterns and tools, making experiments
and interpreting their results
 C3.3 - Applying solution patterns using specific engineering tools and mehods
 C3.4 - Comparatively and experimentaly evaluation of the alternative solutions for
performance optimization
 C3.5 - Developing and implementing information system solutions for concrete problems
C6 - Designing intelligent systems
 C6.1 - Describing intelligent systems’ components
 C6.2 - Using domain-specific tools for explaining the operation of intelligent systems
 C6.3 - Applying fundamental methods and principles for specifying solutions for typical
problems using intelligent systems
 C6.4 - Choosing criteria and methods for the evaluation of quality, performances and
limitations of information systems
 C6.5 - Developing and implementing professional projects for intelligent systems
N/A
7. Discipline objectives (as results from the key skills gained)
7.1
General objective
7.2
Specific objectives
In-depth study of the concepts, techniques, algorithms and
advanced methods for pre-processing, learning and
evaluation, specific to the data mining process
The knowledge and ability to operate within a data mining
process, with the associated tasks, to design a data mining
process flow for a given problem; ability to translate a task
from problem domain specification into solution domain
specification; ability to select appropriate learning
techniques, pre-processing strategies and evaluation
measures for a given domain task. The ability to measure
and compare the quality of solutions, and select the most
appropriate solution given a specific context.
8. Contents
8.1. Course (syllabus)
1
Supervised learning algorithms I
2
Supervised learning algorithms II
3
Supervised vs. unsupervised learning
4
Clustering
8.2. Applications (laboratory works)
1
Feature selection – investigating several algorithms with
Weka
Teaching methods
Powerpoint
presentations,
discussions
Teaching methods
Practical guides
Observations
Observations
-
Bibliography
1. Ian H. Witten, Eibe Frank, Mark A. Hall – Data Mining: Practical Machine Learning Tools and
Techniques (Third Edition), Morgan Kaufmann, January 2011, 629 pg, ISBN 978-0-12-374856-0
2. Jiawei Han and Micheline Kamber – Data Mining: Concepts and Techniques, 2 nd ed., Morgan
Kaufmann Publishers, March 2006, ISBN 1-55860-901-6
Note. The contents have been restricted to the 4 courses and 1 application to be developed
2
9. Bridging course contents with the expectations of the representatives of the
epistemic community, professional associations and employers in the field
10. Evaluation
Activity type 10.1 Evaluation criteria
10.2 Evaluation methods 10.3 Share of the
final grade
Written exam
60%
Course
The ability to understand and
operate with the concepts,
techniques and specific data
mining algorithms; the ability to
analyze the advantages and
disadvantages of an approach
and compare different
approaches
Aplications
The ability to address a new
Assessment
40%
problem which requires a data
during the
mining based solution
semester
10.4 Minimum standard of performance
The understanting of the different steps involved in the data mining process and their roles; the ability
to operate with the main concepts and techniques of the domain.
In order to receive the credit, a student must obtain at least the grade 5 for the applications, and the
final grade must be at least 5.
Date of completion
........................
Lecturer
Prof.dr.ing. Rodica Potolea
Date of approval in the department
.........................
Collaborator
Drd. Mihai Cuibus
Head of department
Prof.dr.ing. Rodica Potolea
3