Annals of Operations Research

, Volume 263, Issue 1–2, pp 1–3 | Cite as

Preface: Data mining and analytics

  • Victoria C. P. Chen
  • Seoung Bum Kim
  • Asil Oztekin
  • Duraikannan Sundaramoorthi

Both data mining and analytics have a basis in statistical methods, where data mining seeks to reveal and characterize unknown interesting structure in data, and analytics encompasses the entire decision-making process from data through decisions, within which the results from a data mining analysis may be used to guide decisions. Both have been enormously successful when used in a variety of applications. The decision-making capabilities of operations research methods can enhance the learning and representation of patterns and structure in data. Vice-versa, the characterizations identified and modeled by data mining and analytics can improve the efficiency of decision-making algorithms. This special volume of the Annals of Operations Research was motivated by The Institute for Operations Research and the Management Sciences (INFORMS) Workshops on Data Mining and Analytics, held at INFORMS annual meetings.

The 25 papers in this special volume include a variety of new methods and...

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Victoria C. P. Chen
    • 1
  • Seoung Bum Kim
    • 2
  • Asil Oztekin
    • 3
  • Duraikannan Sundaramoorthi
    • 4
  1. 1.The University of Texas at ArlingtonArlingtonUSA
  2. 2.Korea UniversitySeoulRepublic of Korea
  3. 3.University of Massachusetts-LowellLowellUSA
  4. 4.Washington University in St. LouisSt. LouisUSA

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