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Advances in Exploratory Pattern Analytics on Ubiquitous Data and Social Media

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Solving Large Scale Learning Tasks. Challenges and Algorithms

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9580))

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Abstract

Exploratory analysis of ubiquitous data and social media includes resources created by humans as well as those generated by sensor devices. This paper reviews recent advances concerning according approaches and methods, and provides additional review and discussion. Specifically, we focus on exploratory pattern analytics implemented using subgroup discovery and exceptional model mining methods, and put these into context. We summarize recent work on description-oriented community detection, spatio-semantic analysis using local exceptionality detection, and class association rule mining for activity recognition. Furthermore, we discuss results and implications.

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Notes

  1. 1.

    http://www.bibsonomy.org.

  2. 2.

    http://www.delicious.com.

  3. 3.

    http://last.fm.

  4. 4.

    http://vikamine.org.

  5. 5.

    http://rsubgroup.org.

  6. 6.

    http://www.everyaware.eu.

  7. 7.

    http://www.ubicon.eu.

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  1. Research Center for Information System Design, University of Kassel, Wilhelmshöher Allee 73, 34121, Kassel, Germany

    Martin Atzmüller

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  1. Martin Atzmüller
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  1. TU Dortmund , Dortmund, Germany

    Stefan Michaelis

  2. TU Dortmund , Dortmund, Germany

    Nico Piatkowski

  3. TU Dortmund , Dortmund, Germany

    Marco Stolpe

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Atzmüller, M. (2016). Advances in Exploratory Pattern Analytics on Ubiquitous Data and Social Media. In: Michaelis, S., Piatkowski, N., Stolpe, M. (eds) Solving Large Scale Learning Tasks. Challenges and Algorithms. Lecture Notes in Computer Science(), vol 9580. Springer, Cham. https://doi.org/10.1007/978-3-319-41706-6_9

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