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SGN-5306 Knowledge Mining, 3 cr |
Ari Visa
No implementations
Assignment and final examination.
Completion parts must belong to the same implementation
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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 | 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 |
The examination is based on the final exam and an exercise work. The grading of the execise work is pass/fail.
Numerical evaluation scale (1-5) will be used on the course
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 |
Course | O/R |
OHJ-1100 Ohjelmointi I | Obligatory |
OHJ-1106 Programming I | Obligatory |
OHJ-1150 Ohjelmointi II | Obligatory |
OHJ-1156 Programming II | Obligatory |
SGN-1107 Introductory Signal Processing | Recommended |
SGN-1200 Signaalinkäsittelyn menetelmät | Recommended |
SGN-1250 Signaalinkäsittelyn sovellukset | Recommended |
Course | Corresponds course | Description |
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Lectures in English or in Finnish.