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Knowledge Discovery from Complex High Dimensional Data

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Solving Large Scale Learning Tasks. Challenges and Algorithms

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9580))

Abstract

Modern data analysis is confronted by increasing dimensionality of problems, mainly contributed by higher resolutions available for data acquisition and by our use of larger models with more degrees of freedom to investigate complex systems deeper. High dimensionality constitutes one aspect of “big data”, which brings us not only computational but also statistical and perceptional challenges. Most data analysis problems are solved using techniques of optimization, where large-scale optimization requires faster algorithms and implementations. Computed solutions must be evaluated for statistical quality, since otherwise false discoveries can be made. Recent papers suggest to control and modify algorithms themselves for better statistical properties. Finally, human perception puts an inherent limit on our understanding to three dimensional spaces, making it almost impossible to grasp complex phenomena. For aid, we use dimensionality reduction or other techniques, but these usually do not capture relations between interesting objects. Here graph-based knowledge representation has lots of potential, for instance to create perceivable and interactive representations and to perform new types of analysis based on graph theory and network topology. In this article, we show glimpses of new developments in these aspects.

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Notes

  1. 1.

    Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo/.

  2. 2.

    The CEL files from the GEO were normalized and summarized for transcripts using the frozen RMA algorithm [55]. Then only the verified (grade A) genes were chosen for further analysis according to the NetAffx probeset annotation v33.1 of Affymetrix (\(n=20492\) afterwards). Also, microarrays with low quality according to the GNUSE [54] error scores \(>1\) were discarded (\(m=392\) afterwards).

  3. 3.

    OSGi Standard http://www.osgi.org/Main/HomePage.

  4. 4.

    DEX Graph Database http://www.sparsity-technologies.com/dex.

  5. 5.

    National Cancer Institute, http://www.cancer.gov.

  6. 6.

    DrugBank, http://www.drugbank.ca.

  7. 7.

    HCI-KDD Network: www.hci-kdd.org.

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Authors and Affiliations

  1. Artificial Intelligence Unit LS8, Computer Science Department, Technische Universität Dortmund, Dortmund, Germany

    Sangkyun Lee

  2. Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

  3. Institute for Information Systems and Computer Media, Graz University of Technology, Graz, Austria

    Andreas Holzinger

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  1. Sangkyun Lee
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  1. TU Dortmund , Dortmund, Germany

    Stefan Michaelis

  2. TU Dortmund , Dortmund, Germany

    Nico Piatkowski

  3. TU Dortmund , Dortmund, Germany

    Marco Stolpe

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Lee, S., Holzinger, A. (2016). Knowledge Discovery from Complex High Dimensional Data. In: Michaelis, S., Piatkowski, N., Stolpe, M. (eds) Solving Large Scale Learning Tasks. Challenges and Algorithms. Lecture Notes in Computer Science(), vol 9580. Springer, Cham. https://doi.org/10.1007/978-3-319-41706-6_7

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