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Database Support for Data Mining Applications

Overview of attention for book
Cover of 'Database Support for Data Mining Applications'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach
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    Chapter 2 Query Languages Supporting Descriptive Rule Mining: A Comparative Study
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    Chapter 3 Declarative Data Mining Using SQL3
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    Chapter 4 Towards a Logic Query Language for Data Mining
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    Chapter 5 A Data Mining Query Language for Knowledge Discovery in a Geographical Information System
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    Chapter 6 Towards Query Evaluation in Inductive Databases Using Version Spaces
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    Chapter 7 The GUHA Method, Data Preprocessing and Mining
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    Chapter 8 Constraint Based Mining of First Order Sequences in SeqLog
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    Chapter 9 Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS
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    Chapter 10 Frequent Itemset Discovery with SQL Using Universal Quantification
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    Chapter 11 Deducing Bounds on the Support of Itemsets
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    Chapter 12 Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data
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    Chapter 13 Condensed Representations for Sets of Mining Queries
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    Chapter 14 One-Sided Instance-Based Boundary Sets
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    Chapter 15 Domain Structures in Filtering Irrelevant Frequent Patterns
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    Chapter 16 Integrity Constraints over Association Rules
Attention for Chapter 10: Frequent Itemset Discovery with SQL Using Universal Quantification
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Citations

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Chapter title
Frequent Itemset Discovery with SQL Using Universal Quantification
Chapter number 10
Book title
Database Support for Data Mining Applications
Published by
Springer, Berlin, Heidelberg, January 2004
DOI 10.1007/978-3-540-44497-8_10
Book ISBNs
978-3-54-022479-2, 978-3-54-044497-8
Authors

Ralf Rantzau, Rantzau, Ralf

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 29%
Professor > Associate Professor 1 14%
Lecturer 1 14%
Student > Master 1 14%
Unknown 2 29%
Readers by discipline Count As %
Computer Science 5 71%
Unknown 2 29%

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