Abstract
Two-way arrays or matrices are often not enough to represent all the information in the data and standard two-way analysis techniques commonly applied on matrices may fail to find the underlying structures in multi-modal datasets. Multiway data analysis has recently become popular as an exploratory analysis tool in discovering the structures in higher-order datasets, where data have more than two modes. We provide a review of significant contributions in the literature on multiway models, algorithms as well as their applications in diverse disciplines including chemometrics, neuroscience, social network analysis, text mining and computer vision.
Keywords
Computer scienceExploratory analysisExploratory data analysisChemometricsData miningData scienceInformation retrievalSocial network analysisMultilinear algebraMachine learningArtificial intelligenceSocial mediaWorld Wide Web
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Publication Info
- Year
- 2008
- Type
- article
- Volume
- 21
- Issue
- 1
- Pages
- 6-20
- Citations
- 441
- Access
- Closed
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Cite This
Evrim Acar,
Bülent Yener
(2008).
Unsupervised Multiway Data Analysis: A Literature Survey.
IEEE Transactions on Knowledge and Data Engineering
, 21
(1)
, 6-20.
https://doi.org/10.1109/tkde.2008.112
Identifiers
- DOI
- 10.1109/tkde.2008.112