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...
| Συγγραφή απο Οργανισμό/Αρχή: | |
|---|---|
| Άλλοι συγγραφείς: | , , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2004.
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| Έκδοση: | 1st ed. 2004. |
| Σειρά: | Lecture Notes in Computer Science,
2682 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
