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Brain network analysis: a data mining perspective

Published: 16 June 2014 Publication History

Abstract

Following the recent advances in neuroimaging technology, the research on brain network analysis becomes an emerging area in data mining community. Brain network data pose many unique challenges for data mining research. For example, in brain networks, the nodes (i.e., the brain regions) and edges (i.e., relationships between brain regions) are usually not given, but should be derived from the neuroimaging data. The network structure can be very noisy and uncertain. Therefore, innovative methods are required for brain network analysis. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as brain network extraction, graph mining, neuroimaging data analysis. In this paper, we review some recent data mining methods which are used in the literature for mining brain network data.

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Published In

cover image ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter  Volume 15, Issue 2
December 2013
60 pages
ISSN:1931-0145
EISSN:1931-0153
DOI:10.1145/2641190
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2014
Published in SIGKDD Volume 15, Issue 2

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Author Tags

  1. brain networks
  2. functional magnetic resonance imaging
  3. graph mining
  4. subgraph patterns

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  • (2022)Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain NetworkFrontiers in Neuroscience10.3389/fnins.2022.88910516Online publication date: 29-Apr-2022
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