|
|
|||||||||||||||||
Course Catalog 2013-2014
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 |
|
|
|
|
|
|
|
|
|
Requirements
Assignment and final examination.
Completion parts must belong to the same implementation
Principles and baselines related to teaching and learning
-
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:
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 |
|
|
|
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| 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. |