Tensors for data mining and data fusion: Models, applications, and scalable algorithms

Evangelos E. Papalexakis, Christos Faloutsos, Nicholas D. Sidiropoulos

Research output: Contribution to journalArticlepeer-review

296 Scopus citations


Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which extract useful latent information out of multiaspect data tensors, have witnessed increasing popularity and adoption by the data mining community. In this survey, we present some of the most widely used tensor decompositions, providing the key insights behind them, and summarizing them from a practitioner's point of view. We then provide an overview of a very broad spectrum of applications where tensors have been instrumental in achieving stateof-The-Art performance, ranging from social network analysis to brain data analysis, and from web mining to healthcare. Subsequently, we present recent algorithmic advances in scaling tensor decompositions up to today's big data, outlining the existing systems and summarizing the key ideas behind them. Finally, we conclude with a list of challenges and open problems that outline exciting future research directions.

Original languageEnglish (US)
Article number921
JournalACM Transactions on Intelligent Systems and Technology
Issue number2
StatePublished - Oct 2016

Bibliographical note

Publisher Copyright:
© 2016 ACM.


  • Multi-Aspect data
  • Multi-way analysis
  • Tensor decomposition
  • Tensor factorization
  • Tensors


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