Exploration of distance metrics in consensus clustering analysis of FMRI data

Elvira Brattico, Chao Liu, Basal Abu Jamous, Asoke Nando

Research output: Contribution to book/anthology/report/proceedingConference abstract in proceedingsResearchpeer-review

Abstract

Clustering techniques have gained great popularity in neuroscience data analysis especially in analysing data from complex experiment paradigm where it is hard to apply traditional model-based method. However, when employing clustering analysis, many clustering algorithms are available nowadays and even with an individual clustering algorithm, choices like parameter settings and distance metrics are very likely to have impacts on the final clustering results. In our previous work, we have demonstrated the benefits of integrating clustering results from multiple clustering algorithms, which provides more stable, reproducible, and complete clustering solutions. In this paper, we aim to further inspect the possible influences from the choices of distance metrics in clustering analysis.

Original languageEnglish
Title of host publication2017 22nd International Conference on Digital Signal Processing, DSP 2017
PublisherIEEE
Publication date2017
Article number8096077
ISBN (Electronic) 2165-3577
Publication statusPublished - 2017

Keywords

  • Bi-CoPaM
  • consensus clustering
  • distance metric
  • fMRI data

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