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Foundations and Advances in Data Mining

Overview of attention for book
Cover of 'Foundations and Advances in Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 The Mathematics of Learning: Dealing with Data *
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    Chapter 2 Logical Regression Analysis: From Mathematical Formulas to Linguistic Rules
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    Chapter 3 A Feature/Attribute Theory for Association Mining and Constructing the Complete Feature Set
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    Chapter 4 A New Theoretical Framework for K-Means-Type Clustering
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    Chapter 5 Clustering Via Decision Tree Construction
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    Chapter 6 Incremental Mining on Association Rules
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    Chapter 7 Mining Association Rules from Tabular Data Guided by Maximal Frequent Itemsets
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    Chapter 8 Sequential Pattern Mining by Pattern-Growth: Principles and Extensions *
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    Chapter 9 Web Page Classification *
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    Chapter 10 Web Mining – Concepts, Applications and Research Directions
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    Chapter 11 Privacy-Preserving Data Mining
Attention for Chapter 7: Mining Association Rules from Tabular Data Guided by Maximal Frequent Itemsets
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Citations

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Chapter title
Mining Association Rules from Tabular Data Guided by Maximal Frequent Itemsets
Chapter number 7
Book title
Foundations and Advances in Data Mining
Published by
Springer, Berlin, Heidelberg, September 2005
DOI 10.1007/11362197_7
Book ISBNs
978-3-54-025057-9, 978-3-54-032393-8
Authors

Q. Zou, Y. Chen, W.W. Chu, X. Lu

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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 %
Student > Ph. D. Student 2 100%
Readers by discipline Count As %
Computer Science 2 100%

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