Knowledge Extraction

Code:

EIC0054

Acronym:

ECON

Keywords
Classification	Keyword
OFICIAL	Artificial Intelligence

Instance: 2006/2007 - 1S

Active?	Yes
Web Page:	http://paginas.fe.up.pt/~ec/index.html
Responsible unit:	Department of Mechanical Engineering
Course/CS Responsible:	Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
LEIC	7	Plano de estudos de transição para 2006/07	5	6	6	-
MIEIC	7	Syllabus since 2006/2007	5	-	6	-
		Plano de estudos de transição para 2006/07	5	-	6	-

Teaching language

Portuguese

Objectives

To provide the students with knowledge so that they can use analysis and extraction techniques of large data quantities’ patterns.

Program

Introduction to the knowledge extraction: Concept of Data Mining; Data Mining and Knowledge Discovery process.

Data preparation: Data cleaning; Data Normalization, Reduction and Discretization.

Association Rules: Definition of the association rules research problem. Quality measures of the association rules. Some research algorithms of association rules.

Clustering: Clustering Techniques. Partition clustering algorithms (K-means, K-medoids) and Hierarchical clustering quality. Other algorithms: BIRCH, CURE, DBSCAN.

Web Mining: Data Mining concepts on the Web; Information research on the Web; Research usage patterns on the Web; Structure analysis and research on the Web.

Text Data Mining: definition, application techniques and examples.

Classification: Classification techniques for the analysis of large data quantities; Decision Trees;

Classification and Regression Trees (CART); Pruning principles; Bayesian Classification. Inductive Logic Programming.

Data Display: Display Techniques for the identification of patterns or exceptions in large data quantities.

PKDD: Parallel Knowledge Discovery in Databases – Parallel Processing Techniques for the extraction of patterns in large data quantities.

KDD Applications.

Mandatory literature

Han, Jiawei; Data mining. ISBN: 1-55860-489-8

Teaching methods and learning activities

Theoretical classes: Exposition of theoretical concepts.
Practical classes: Exercise resolution, discussion of themes presented in the theoretical classes and help on the practical assignments.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Subject Classes	Participação presencial	56,00
	Total:	-	0,00

Eligibility for exams

The average grade of the distributed evaluation component (assignment and article presentation) must be equal or superior to 6 marks.

Calculation formula of final grade

0.35* Assignment Grade + 0.15* Article Presentation + 0.5* Exam Grade

Special assessment (TE, DA, ...)

The students dismissed from the practical classes must do the practical assignment and the article presentation.

Classification improvement

The distributed classification improvement can only be done in the following year.