Improving clustering by imposing network information

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Science Advances  07 Aug 2015:
Vol. 1, no. 7, e1500163
DOI: 10.1126/sciadv.1500163


Cluster analysis is one of the most popular data analysis tools in a wide range of applied disciplines. We propose and justify a computationally efficient and straightforward-to-implement way of imposing the available information from networks/graphs (a priori available in many application areas) on a broad family of clustering methods. The introduced approach is illustrated on the problem of a noninvasive unsupervised brain signal classification. This task is faced with several challenging difficulties such as nonstationary noisy signals and a small sample size, combined with a high-dimensional feature space and huge noise-to-signal ratios. Applying this approach results in an exact unsupervised classification of very short signals, opening new possibilities for clustering methods in the area of a noninvasive brain-computer interface.

  • time series analysis
  • finite element method
  • unsupervised classification
  • clustering
  • graph
  • Network
  • regularization
  • EEG
  • Neuroscience

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