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Knowledge Discovery in Inductive Databases

Overview of attention for book
Cover of 'Knowledge Discovery in Inductive Databases'

Table of Contents

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    Book Overview
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    Chapter 1 Value, Cost, and Sharing: Open Issues in Constrained Clustering
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    Chapter 2 Mining Bi-sets in Numerical Data
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    Chapter 3 Extending the Soft Constraint Based Mining Paradigm
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    Chapter 4 On Interactive Pattern Mining from Relational Databases
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    Chapter 5 Analysis of Time Series Data with Predictive Clustering Trees
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    Chapter 6 Integrating Decision Tree Learning into Inductive Databases
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    Chapter 7 Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets
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    Chapter 8 An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results
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    Chapter 9 Beam Search Induction and Similarity Constraints for Predictive Clustering Trees
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    Chapter 10 Frequent Pattern Mining and Knowledge Indexing Based on Zero-Suppressed BDDs
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    Chapter 11 Extracting Trees of Quantitative Serial Episodes
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    Chapter 12 IQL: A Proposal for an Inductive Query Language
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    Chapter 13 Mining Correct Properties in Incomplete Databases
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    Chapter 14 Efficient Mining Under Rich Constraints Derived from Various Datasets
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    Chapter 15 Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth
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    Chapter 16 Towards a General Framework for Data Mining
Attention for Chapter 13: Mining Correct Properties in Incomplete Databases
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Citations

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Chapter title
Mining Correct Properties in Incomplete Databases
Chapter number 13
Book title
Knowledge Discovery in Inductive Databases
Published by
Springer, Berlin, Heidelberg, September 2006
DOI 10.1007/978-3-540-75549-4_13
Book ISBNs
978-3-54-075548-7, 978-3-54-075549-4
Authors

François Rioult, Bruno Crémilleux

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 2 100%
Readers by discipline Count As %
Computer Science 2 100%

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