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Quality Measures in Data Mining
Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2007.
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| Edición: | 1st ed. 2007. |
| Colección: | Studies in Computational Intelligence,
43 |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- Overviews on rule quality
- Choosing the Right Lens: Finding What is Interesting in Data Mining
- A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study
- Association Rule Interestingness Measures: Experimental and Theoretical Studies
- On the Discovery of Exception Rules: A Survey
- From data to rule quality
- Measuring and Modelling Data Quality for Quality-Awareness in Data Mining
- Quality and Complexity Measures for Data Linkage and Deduplication
- Statistical Methodologies for Mining Potentially Interesting Contrast Sets
- Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality
- Rule quality and validation
- A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link
- Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules
- Association Rule Interestingness: Measure and Statistical Validation
- Comparing Classification Results between N-ary and Binary Problems.