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Enhancement of the K-Means Algorithm for Mixed Data in Big Data Platforms

  • Oded Koren
  • Carina Antonia Hallin
  • Nir PerelEmail author
  • Dror Bendet
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 868)

Abstract

Big data research has emerged as an important discipline in information systems research and management. Yet, while the torrent of data being generated on the Internet is increasingly unstructured and non-numeric in the form of images and texts, research indicates there is an increasing need to develop more efficient algorithms for treating mixed data in big data. In this paper, we apply the classical K-means algorithm to both numeric and categorical attributes in big data platforms. We first present an algorithm which handles the problem of mixed data. We then utilize big data platforms to implement the algorithm. This provides us with a solid basis for performing more targeted profiling for business and research purposes using big data, so that decision makers will be able to treat mixed data, i.e. numerical and categorical data, to explain phenomena within the big data ecosystem.

Keywords

Big data Mixed data Hadoop K-means 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Oded Koren
    • 1
  • Carina Antonia Hallin
    • 2
  • Nir Perel
    • 1
    Email author
  • Dror Bendet
    • 1
  1. 1.School of Industrial Engineering and ManagementShenkar – Engineering, Design, ArtRamat GanIsrael
  2. 2.Department of International Economics, Government and BusinessCopenhagen Business SchoolFrederiksbergDenmark

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