Introduction

  • Petros Xanthopoulos
  • Panos M. Pardalos
  • Theodore B. Trafalis
Chapter
Part of the SpringerBriefs in Optimization book series (BRIEFSOPTI)

Abstract

Data mining (DM), conceptually, is a very general term that encapsulates a large number of methods, algorithms, and technologies. The common denominator among all these is their ability to extract useful patterns and associations from data usually stored in large databases. Thus DM techniques aim to provide knowledge and interesting interpretation of, usually, vast amounts of data. This task is crucial, especially today, mainly because of the emerging needs and capabilities that technological progress creates. In this monograph we investigate some of the most well-known data mining algorithms from an optimization perspective and we study the application of robust optimization (RO) in them. This combination is essential in order to address the unavoidable problem of data uncertainty that arises in almost all realistic problems that involve data analysis. In this chapter we provide some historical perspectives of data mining and its foundations and at the same time we “touch” the concepts of robust optimization and discuss its differences compared to stochastic programming.

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Copyright information

© Petros Xanthopoulos,Panos M. Pardalos,Theodore B. Trafalis 2013

Authors and Affiliations

  • Petros Xanthopoulos
    • 1
  • Panos M. Pardalos
    • 2
    • 3
  • Theodore B. Trafalis
    • 4
    • 5
  1. 1.Department of Industrial Engineering and Management SystemsUniversity of Central FloridaOrlandoUSA
  2. 2.Center for Applied Optimization Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Laboratory of Algorithms and Technologies for Networks Analysis (LATNA)National Research University Higher School of EconomicsMoscowRussia
  4. 4.School of Industrial and Systems EngineeringThe University of OklahomaNormanUSA
  5. 5.School of MeteorologyThe University of OklahomaNormanUSA

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