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Discovering hierarchical structure in normal relational data

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Authors:
  • Schmidt, Mikkel Nørgaard ;
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    Orcid logo0000-0001-6927-8869
    Department of Applied Mathematics and Computer Science, Technical University of Denmark
  • Herlau, Tue ;
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    Department of Applied Mathematics and Computer Science, Technical University of Denmark
  • Mørup, Morten
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    Orcid logo0000-0003-4985-4368
    Department of Applied Mathematics and Computer Science, Technical University of Denmark
DOI:
10.1109/CIP.2014.6844498
Abstract:
Hierarchical clustering is a widely used tool for structuring and visualizing complex data using similarity. Traditionally, hierarchical clustering is based on local heuristics that do not explicitly provide assessment of the statistical saliency of the extracted hierarchy. We propose a non-parametric generative model for hierarchical clustering of similarity based on multifurcating Gibbs fragmentation trees. This allows us to infer and display the posterior distribution of hierarchical structures that comply with the data. We demonstrate the utility of our method on synthetic data and data of functional brain connectivity.
Type:
Conference paper
Language:
English
Published in:
Proceedings of the 4th International Workshop on Cognitive Information Processing, 2014
Keywords:
Bioengineering; Communication, Networking and Broadcast Technologies; Computing and Processing; Robotics and Control Systems; Signal Processing and Analysis
Main Research Area:
Science/technology
Conference:
4th International Workshop on Cognitive Information Processing (CIP 2014)
Publisher:
IEEE
Submission year:
2014
ID:
2282174276

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