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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

  1. Altmetric Badge
    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 14: One-Sided Instance-Based Boundary Sets
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Chapter title
One-Sided Instance-Based Boundary Sets
Chapter number 14
Book title
Database Support for Data Mining Applications
Published by
Springer, Berlin, Heidelberg, January 2004
DOI 10.1007/978-3-540-44497-8_14
Book ISBNs
978-3-54-022479-2, 978-3-54-044497-8
Authors

Evgueni N. Smirnov, Ida G. Sprinkhuizen-Kuyper, H Jaap van den Herik

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 100%
Readers by discipline Count As %
Computer Science 1 100%

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