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...
Tallennettuna:
| Päätekijä: | |
|---|---|
| Muut tekijät: | |
| Aineistotyyppi: | Livre numérique |
| Kieli: | Anglais |
| Julkaistu: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Painos: | 1st ed. 2007. |
| Sarja: | Studies in Computational Intelligence
43 |
| Linkit: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Huomautus: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Quality measures in data mining, Fabrice Guillet, Howard J. Hamilton (eds.), Berlin, Springer, 2007, 1 vol. (xiv-313 p.), Studies in computational intelligence, 978-3-540-44911-9 • Quality Measures in Data Mining, Texte imprimé, 9783540830603 • Quality Measures in Data Mining, Texte imprimé, 9783642079528 • Quality measures in data mining, Fabrice Guillet, Howard J. Hamilton (eds.), Berlin, Springer, 2007, 1 vol. (xiv-313 p.), Studies in computational intelligence, 978-3-540-44911-9 |
Sisällysluettelo:
- 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