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Data Mining and Knowledge Discovery via Logic-Based Methods Theory, Algorithms, and Applications /
The importance of having efficient and effective methods for data mining and knowledge discovery (DM) is rapidly growing. This is due to the wide use of fast and affordable computing power and data storage media and also the gathering of huge amounts of data in almost all aspects of human activity a...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer US : Imprint: Springer,
2010.
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| Edición: | 1st ed. 2010. |
| Colección: | Springer Optimization and Its Applications,
43 |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- Algorithmic Issues
- Inferring a Boolean Function from Positive and Negative Examples
- A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examples
- Some Fast Heuristics for Inferring a Boolean Function from Examples
- An Approach to Guided Learning of Boolean Functions
- An Incremental Learning Algorithm for Inferring Boolean Functions
- A Duality Relationship Between Boolean Functions in CNF and DNF Derivable from the Same Training Examples
- The Rejectability Graph of Two Sets of Examples
- Application Issues
- The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis
- Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions
- Some Application Issues of Monotone Boolean Functions
- Mining of Association Rules
- Data Mining of Text Documents
- First Case Study: Predicting Muscle Fatigue from EMG Signals
- Second Case Study: Inference of Diagnostic Rules for Breast Cancer
- A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis
- Conclusions.