Course Catalog 2014-2015
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Course Catalog 2014-2015

SGN-43006 Knowledge Mining and Big Data, 5 cr

Additional information

Lectures in English or in Finnish.
Suitable for postgraduate studies

Person responsible

Ari Visa

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises
 4 h/per
 2 h/per


 


 


 


 
SGN-43006 2014-01 Monday 10 - 12 , TB224
Thursday 10 - 12 , TB104
Monday 10 - 12 , TB111
Monday 10 - 12 , TB109
Monday 10 - 12 , TB104

Requirements

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

Learning Outcomes

Learning outcomes: The student can describe the difference between data and knowledge mining. The student can list and describe OLAP, association, predictive modeling, modeling, regression analysis and cluster analysis. The student can analyse the own problem and apply the lectured method on it. The student is capable to analyse the proposed solutions.

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   

Instructions for students on how to achieve the learning outcomes

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   Yes    English  
Book   "Principles of Data Mining"   David J. Hand, Heikki Mannila and Padhraic Smyth       MIT Press, 2000   Yes    English  

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-43006 Knowledge Mining and Big Data, 5 cr SGN-5306 Knowledge Mining, 3 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-43006 2014-01 Learning outcomes: The student can describe the difference between data and knowledge mining. The student can list and describe OLAP, association, predictive modeling, modeling, regression analysis and cluster analysis. The student can analyse the own problem and apply the lectured method on it. The student is capable to analyse the proposed solutions.        

Last modified10.03.2014