Analysis of Patient Groups and Immunization Results Based on Subspace Clustering View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015-08-21

AUTHORS

Michael Hund , Werner Sturm , Tobias Schreck , Torsten Ullrich , Daniel Keim , Ljiljana Majnaric , Andreas Holzinger

ABSTRACT

Biomedical experts are increasingly confronted with what is often called Big Data, an important subclass of high-dimensional data. High-dimensional data analysis can be helpful in finding relationships between records and dimensions. However, due to data complexity, experts are decreasingly capable of dealing with increasingly complex data. Mapping higher dimensional data to a smaller number of relevant dimensions is a big challenge due to the curse of dimensionality. Irrelevant, redundant, and conflicting dimensions affect the effectiveness and efficiency of analysis. Furthermore, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We show the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we analyze relationships between patients, sets of patient attributes, and outcomes of a vaccination treatment by means of a subspace clustering approach. We present an analysis workflow and discuss future directions for high-dimensional (medical) data analysis and visual exploration. More... »

PAGES

358-368

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-23344-4_35

DOI

http://dx.doi.org/10.1007/978-3-319-23344-4_35

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1014292595


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