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Data Mining and Knowledge Discovery via Logic-Based Methods

Evangelos Triantaphyllou ()
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Evangelos Triantaphyllou: Louisiana State University

in Springer Optimization and Its Applications from Springer, currently edited by Pardalos, Panos, Thai, My T. and Du, Ding-Zhu

Date: 2010
ISBN: 978-1-4419-1630-3
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Citations: View citations in EconPapers (3)

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Chapters in this book:

Ch Chapter 1 Introduction
Evangelos Triantaphyllou
Ch Chapter 10 Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions
Evangelos Triantaphyllou
Ch Chapter 11 Some Application Issues of Monotone Boolean Functions
Evangelos Triantaphyllou
Ch Chapter 12 Mining of Association Rules
Evangelos Triantaphyllou
Ch Chapter 13 Data Mining of Text Documents
Evangelos Triantaphyllou
Ch Chapter 14 First Case Study: Predicting Muscle Fatigue from EMG Signals
Evangelos Triantaphyllou
Ch Chapter 15 Second Case Study: Inference of Diagnostic Rules for Breast Cancer
Evangelos Triantaphyllou
Ch Chapter 16 A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis
Evangelos Triantaphyllou
Ch Chapter 17 Conclusions
Evangelos Triantaphyllou
Ch Chapter 2 Inferring a Boolean Function from Positive and Negative Examples
Evangelos Triantaphyllou
Ch Chapter 3 A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examples
Evangelos Triantaphyllou
Ch Chapter 4 Some Fast Heuristics for Inferring a Boolean Function from Examples
Evangelos Triantaphyllou
Ch Chapter 5 An Approach to Guided Learning of Boolean Functions
Evangelos Triantaphyllou
Ch Chapter 6 An Incremental Learning Algorithm for Inferring Boolean Functions
Evangelos Triantaphyllou
Ch Chapter 7 A Duality Relationship Between Boolean Functions in CNF and DNF Derivable from the Same Training Examples
Evangelos Triantaphyllou
Ch Chapter 8 The Rejectability Graph of Two Sets of Examples
Evangelos Triantaphyllou
Ch Chapter 9 The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis
Evangelos Triantaphyllou

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DOI: 10.1007/978-1-4419-1630-3

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