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Cited by 434

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Unsupervised Multiway Data Analysis: A Literature Survey

Acar, E., Yener, B.·2009·IEEE Transactions on Knowledge and Data Engineering
DOI 10.1109/tkde.2008.112
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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.

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Metadata

Authors
Acar, E., & Yener, B.
Venue
IEEE Transactions on Knowledge and Data Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2009-01
Volume
21
Issue
1
Pages
6-20
ISSN
1041-4347 (ISSN-L)
DOI
10.1109/tkde.2008.112
OpenAlex
W2140862024
Cited by
434
References
2
FWCI
8.70
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Unsupervised Multiway Data Analysis: A Literature Survey. https://app.cochat.ai/papers/10.1109/tkde.2008.112
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Acar, E., & Yener, B. (2009). Unsupervised Multiway Data Analysis: A Literature Survey. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2008.112

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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