Course Catalog 2008-2009 Basic Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Course´s person responsible

Ari Visa

Implementations

No implementations

Requirements

Assignment and final examination.
Completion parts must belong to the same implementation

Principles and baselines related to teaching and learning

-

Objectives

The course equips the student with a sound understanding of data mining methods and principles and teaches methods for knowledge discovery in large corporate databases.

Content

Content	Core content	Complementary knowledge	Specialist knowledge
1.	Concept Description	Data preprocessing Data Generalization Summarization-Based Characterization Analyzing of Attribute Relevance
2.	Mining Association Rules	Mining Single-Dimensional Boolean Association Rules, and Multilevel Association Rules, and Multidimensional Association Rules Correlation Analysis
3.	Descriptive Models	Cluster Analysis Describing Data by Probability Distributions and Densities	Parametric models Nonparametric models
4.	Predictive Models	Regression models Stochastic models Predictive models for classification Models for structured data

Evaluation criteria for the course

The examination is based on the final exam and an exercise work. The grading of the execise work is pass/fail.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Partial passing:

Completion parts must belong to the same implementation

Study material

Type	Name	Author	ISBN	URL	Edition, availability, ...	Examination material	Language
Book	"Data Mining: Concepts and Techniques"	Jiawei Han & Micheline Kamber			Morgan Kaufmann Publisher, 2000		English
Book	"Principles of Data Mining"	David J. Hand, Heikki Mannila and Padhraic Smyth			MIT Press, 2000		English

Prerequisites

Correspondence of content

Additional information

Lectures in English or in Finnish.

Last modified	13.08.2008
Modifier	Hanna Hämäläinen