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Foundations and Advances in Data Mining

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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 1: The Mathematics of Learning: Dealing with Data *
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Chapter title
The Mathematics of Learning: Dealing with Data *
Chapter number 1
Book title
Foundations and Advances in Data Mining
Published by
Springer, Berlin, Heidelberg, September 2005
DOI 10.1007/11362197_1
Book ISBNs
978-3-54-025057-9, 978-3-54-032393-8
Authors

T. Poggio, S. Smale

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