Database Support for Data Mining Applications : Discovering Knowledge with Inductive Queries
Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge. This book on database support for...
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| Autor principal: | |
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| Altres autors: | , |
| Format: | Livre numérique |
| Idioma: | Anglais |
| Publicat: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Col·lecció: | Lecture notes in computer science. Lecture notes in artificial intelligence
2682 |
| Matèries: | |
| Accés en línia: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Database support for data mining applications, Discovering knowledge with inductive queries, Rosa Meo, Pier Luca Lanzi, Mika Klemettinen (eds.), Berlin, Springer, 2004, 1 volume (XII-323 pages), Lecture notes in computer science, 3-540-22479-3 • Database Support for Data Mining Applications, Texte imprimé, 9783662188934 |
Taula de continguts:
- Database Languages and Query Execution
- Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach
- Query Languages Supporting Descriptive Rule Mining: A Comparative Study
- Declarative Data Mining Using SQL3
- Towards a Logic Query Language for Data Mining
- A Data Mining Query Language for Knowledge Discovery in a Geographical Information System
- Towards Query Evaluation in Inductive Databases Using Version Spaces
- The GUHA Method, Data Preprocessing and Mining
- Constraint Based Mining of First Order Sequences in SeqLog
- Support for KDD-Process
- Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS
- Frequent Itemset Discovery with SQL Using Universal Quantification
- Deducing Bounds on the Support of Itemsets
- Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data
- Condensed Representations for Sets of Mining Queries
- One-Sided Instance-Based Boundary Sets
- Domain Structures in Filtering Irrelevant Frequent Patterns
- Integrity Constraints over Association Rules.