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Robust Data Mining

  • Book
  • © 2013

Overview

Authors:
  1. Petros Xanthopoulos

    1. , Department of Industrial Engineering, University of Central Florida, Orlando, USA

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  2. Panos M. Pardalos

    1. , Department of Industrial & Systems Engin, University of Florida, Gainesville, USA

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  3. Theodore B. Trafalis

    1. , Industrial Engineering, University of Ocklahoma, Norman, USA

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  • Summarizes the latest applications of robust optimization in data mining
  • An essential accompaniment for theoreticians and data miners
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Optimization (BRIEFSOPTI)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xii
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  2. Introduction

    • Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis
    Pages 1-7

  3. Least Squares Problems

    • Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis
    Pages 9-20

  4. Principal Component Analysis

    • Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis
    Pages 21-26

  5. Linear Discriminant Analysis

    • Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis
    Pages 27-33

  6. Support Vector Machines

    • Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis
    Pages 35-48

  7. Conclusion

    • Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis
    Pages 49-49

  8. Back Matter

    Pages 51-59
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Keywords

About this book

Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise.

This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents  the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems.

This brief will appeal to theoreticians and data miners working in this field.

Reviews

From the reviews:

“The goal of the book is to provide a guide for junior researchers interested in pursuing theoretical research in data mining and robust optimization and has been developed so that each chapter can be studied independent of the others.” (Hans Benker, Zentralblatt MATH, Vol. 1260, 2013)

Authors and Affiliations

  • , Department of Industrial Engineering, University of Central Florida, Orlando, USA

    Petros Xanthopoulos

  • , Department of Industrial & Systems Engin, University of Florida, Gainesville, USA

    Panos M. Pardalos

  • , Industrial Engineering, University of Ocklahoma, Norman, USA

    Theodore B. Trafalis

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