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Unsupervised Tensor Mining for Big Data Practitioners.

Evangelos E Papalexakis1, Christos Faloutsos1

  • 11 Department of Computer Science, Carnegie Mellon University , Pittsburgh, Pennsylvania.

Big Data
|September 20, 2016
PubMed
Summary
This summary is machine-generated.

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Tensors offer a powerful way to model and analyze complex Big Data with multiple aspects. This research popularizes tensor decompositions for Big Data practitioners, addressing challenges and proposing tools for automated tensor mining.

Area of Science:

  • Data Science
  • Applied Mathematics
  • Computer Science

Background:

  • Multiaspect data is common in Big Data applications, such as social networks.
  • Existing methods struggle to jointly model diverse data aspects effectively.

Purpose of the Study:

  • To popularize tensors and tensor decompositions for Big Data practitioners.
  • To demonstrate the effectiveness of tensors in modeling multiaspect data.
  • To present solutions for challenges in applying tensor methods to Big Data.

Main Methods:

  • Utilizing tensors as multidimensional extensions of matrices for data modeling.
  • Applying tensor decomposition techniques for data analysis.
  • Developing automated, unsupervised tensor mining tools.
Keywords:
big data analyticsdata miningmachine learning

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Main Results:

  • Tensors provide a principled approach to modeling multiaspect Big Data.
  • Tensor decompositions can effectively leverage information from multiple data aspects.
  • Recent work addresses key challenges in Big Data tensor applications.

Conclusions:

  • Tensors and their decompositions are valuable tools for Big Data analysis.
  • This work contributes to the development of automated tensor mining.
  • The goal is broad adoption by academia and industry practitioners.