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Visualization and Visual Analytic Techniques for Patterns

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High-Utility Pattern Mining

Part of the book series: Studies in Big Data ((SBD,volume 51))

Abstract

This chapter surveys visualization techniques for frequent itemsets, association rules, and sequential patterns. The human is crucial in the process of identifying interesting patterns and thus, mining such patterns and visualizing them is important for the decision making. The complementary feedback loop that a user may use to refine parameters through inspecting the current mining results is broadly described as visual analytics. This survey identifies visual designs for patterns of each category and analyzes and compares their strengths and weaknesses systematically. The comparison and overview help decision-makers selecting the appropriate technique for their tasks and systems while knowing about their limitations.

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Notes

  1. 1.

    http://ieeevis.org.

  2. 2.

    https://www.eurovis2018.org/.

  3. 3.

    http://www.kdd.org/.

  4. 4.

    https://www.computer.org/web/tvcg.

  5. 5.

    http://journals.sagepub.com/home/ivia.

  6. 6.

    http://setviz.net, accessed Feb. 2018.

  7. 7.

    http://www.almaden.ibm.com/cs/quest/demo/assoc/general.html, accessed Feb. 2018.

  8. 8.

    ftp://ftp.sgi.com/sgi/mineset/overview/mineset_overview.htm, accessed Feb. 2018.

  9. 9.

    A quantitative comparison would require the implementation of each technique, standardized datasets, as well as a methodology to measure the scalability, for example, by measuring the occlusion through pixel overplotting [25]. We consider this as future work.

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  1. Universität Konstanz, Universitätsstr. 10, 78467, Konstanz, Germany

    Wolfgang Jentner & Daniel A. Keim

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  1. Wolfgang Jentner
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  1. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  2. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. Université du Québec à Montréal, Montreal, QC, Canada

    Roger Nkambou

  4. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Bay Vo

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

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Jentner, W., Keim, D.A. (2019). Visualization and Visual Analytic Techniques for Patterns. In: Fournier-Viger, P., Lin, JW., Nkambou, R., Vo, B., Tseng, V. (eds) High-Utility Pattern Mining. Studies in Big Data, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-030-04921-8_12

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