Dimensionality Reduction for Exploratory Data Analysis in Daily Medical Research

Dominic Girardi, Andreas Holzinger

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In contrast to traditional, industrial applications such as market basket analysis, the process of knowledge discovery in medical research is mostly performed by the medical domain experts themselves. This is mostly due to the high complexity of the research domain, which requires deep domain knowledge. At the same time, these domain experts face major obstacles in handling and analyzing their high-dimensional, heterogeneous, and complex research data. In this paper, we present a generic, ontology-centered data infrastructure for scientific research which actively supports the medical domain experts in data acquisition, processing and exploration. We focus on the system’s capabilities to automatically perform dimensionality reduction algorithms on arbitrary high-dimensional data sets and allow the domain experts to visually explore their high-dimensional data of interest, without needing expert IT or specialized database knowledge.
Original languageEnglish
Title of host publicationAdvanced Data Analytics in Health
EditorsPhilippe J. Giabbanelli, Vijay K Mago, Elpiniki I. Papageorgiou
Place of PublicationCham
PublisherSpringer International
Pages3-20
ISBN (Electronic)978-3-319-77911-9
ISBN (Print)978-3-319-77910-2
Publication statusPublished - 21 Apr 2018

Keywords

  • Dimensionality Reduction
  • Knowledge Discovery
  • Data Science
  • Information Science

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  • Cite this

    Girardi, D., & Holzinger, A. (2018). Dimensionality Reduction for Exploratory Data Analysis in Daily Medical Research. In P. J. Giabbanelli, V. K. Mago, & E. I. Papageorgiou (Eds.), Advanced Data Analytics in Health (pp. 3-20). Cham: Springer International.