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      Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining

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      Author
      Aljobouri, H. K.
      Jaber, H. A.
      Koçak, O. M.
      Algin, O.
      Çankaya, I.
      Date
      2018
      Source Title
      Journal of Neuroscience Methods
      Print ISSN
      0165-0270
      Publisher
      Elsevier
      Volume
      299
      Pages
      45 - 54
      Language
      English
      Type
      Article
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      Abstract
      Background: Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the appropriate clustering algorithms remains a problem. New method: A novel application of the robust unsupervised learning approach is proposed in the current study. Robust growing neural gas (RGNG) algorithm was fed into fMRI data and compared with growing neural gas (GNG) algorithm, which has not been used for this purpose or any other medical application. Learning algorithms proposed in the current study are fed with real and free auditory fMRI datasets. Results: The fMRI result obtained by running RGNG was within the expected outcome and is similar to those found with the hypothesis method in detecting active areas within the expected auditory cortices. Comparison with existing method(s): The fMRI application of the presented RGNG approach is clearly superior to other approaches in terms of its insensitivity to different initializations and the presence of outliers, as well as its ability to determine the actual number of clusters successfully, as indicated by its performance measured by minimum description length (MDL) and receiver operating characteristic (ROC) analysis. Conclusions: The RGNG can detect the active zones in the brain, analyze brain function, and determine the optimal number of underlying clusters in fMRI datasets. This algorithm can define the positions of the center of an output cluster corresponding to the minimal MDL value.
      Keywords
      Clustering technique
      Data mining
      Growing neural gas (GNG)
      Robust growing neural gas (RGNG)
      Embargo Lift Date
      2019-10-01
      Permalink
      http://hdl.handle.net/11693/49912
      Published Version (Please cite this version)
      https://doi.org/10.1016/j.jneumeth.2018.02.007
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