Data Mining II

Code:

CC4024

Acronym:

CC4024

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2015/2016 - 2S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master in Computer Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:CC	9	Study plan since 2014/2015	1	-	6	42	162
M:ENM	6	Official Study Plan since 2013-2014	1	-	6	42	162
MI:ERS	9	Plano Oficial desde ano letivo 2014	4	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

The student should be able to:

 

 

1. 
   

   Recognize different problems solvable through the use of data mining techniques discussed and detailed in content.

 

1. 
   

   Identify and specify data mining tasks similar to those discussed.

 

1. 
   

   Obtain and pre-process data for the algorithms and tasks addressed.

 

1. 
   

   Understand and use data mining algorithms.

 

1. 
   

   Obtain, interpret, evaluate and use data mining models.

 

1. 
   

   Implement some of the algorithms and propose changes to improve them.

 

Learning outcomes and competences

The student is be able to:

 

 

1. 
   

   Recognize different problems solvable through the use of data mining techniques discussed and detailed in content.

 

1. 
   

   Identify and specify data mining tasks similar to those discussed.

 

1. 
   

   Obtain and pre-process data for the algorithms and tasks addressed.

 

1. 
   

   Understand and use data mining algorithms.

 

1. 
   

   Obtain, interpret, evaluate and use data mining models.

 

1. 
   

   Implement some of the algorithms and propose changes to improve them.

 

Working method

Presencial

Program

 

Frequent Pattern Mining, Association Rules, Sequential Patterns, Web Mining, Recommender Systems, Link Analysis, Information retrieval, text mining.

Mandatory literature

000105691. ISBN: 9783642194597 hbk

Complementary Bibliography

000075501. ISBN: 9780262082907 hbk

Teaching methods and learning activities

Distributed evaluation with final exam

Final grade

Continuous Assessment = Assignments + Quizzes or mini-tests (40%)

Final Exam (40%)

 

 

Minimum grade at each component: 6/20

Software

R

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	40,00
Participação presencial	0,00
Trabalho laboratorial	60,00
Total:	100,00

Eligibility for exams

 

Minimum grade at each component: 6/20

Calculation formula of final grade

Continuous evaluation = Assignments and Quizzes/tests (60%)

Final Exam - (40%)

Minimal grade in each component 6 out of 20.

Classification improvement

Continuous evaluation has no appeal.

The grade in the normal exam can be replaced in the appeal exam.