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ISSN 2587-814X (print),
ISSN 2587-8158 (online)

Russian version: ISSN 1998-0663 (print),
ISSN 2587-8166 (online)

Vladimir Laptev 1, Paul Orlov 1
  • 1 St. Petersburg State Polytechnical University , 29, Politekhnicheskaya Street, St. Petersburg, 195251, Russian Federation.

Cluster analysis of visual perception of data structure

2015. No. 3(33) . P. 34–43 [issue contents]

Vladimir V. Laptev - Associate Professor, Department of Engineering Graphics and Design, Institute of Metallurgy, Mechanical Engineering and Transport, St. Petersburg State Polytechnical University
Address: 29, Politekhnicheskaya Street, St. Petersburg, 195251, Russian Federation.
E-mail: laptevsee@yandex.ru

Paul A. Orlov - Senior Lecturer, Department of Engineering Graphics and Design, Institute of Metallurgy, Mechanical Engineering and Transport, St. Petersburg State Polytechnical University; Senior Lecturer, Department of Media Design and Information Technologies, School of Journalism & Mass Communication, St.Petersburg State University
Address: 29, Politekhnicheskaya Street, St. Petersburg, 195251, Russian Federation.
E-mail: paul.a.orlov@gmail.com

      Data structures are common indicators in the fields of management and business. Infographics (serious graphics), a special area of Communication Design, provides a number of graphical ways to visualize this type of data. The application of each available chart type corresponds to certain limitations, which are associated with features of visual perception and semiotic aspects. In our study, we chose the Sankey flow diagram because of an insufficient degree of scrutiny. This type of diagram is often used to represent data structure in business processes. We built an eye-tracking study to identify the methods of assessment forms of graphical image of data structure visualization. In our experiment, we used a 4-flow Sankey diagram as a stimulus.
      Hierarchical divisive algorithms were taken as the method of analysis. This method works with a universal cluster consisting of all gaze fixation, followed by step partitioning it into smaller pieces. It has been found that there are at least four clusters based on the coordinates. In the present model, we found an “input” cluster and an “output cluster group” and clearly defined the central cluster of gaze fixations. Increasing the number of clusters changes the picture in the direction of greater detail. We show a certain narrative that is traced when viewing charts. This narrative identifies the sequence of flows “movement” from the whole to its structural parts. As a result, cluster analysis allows the visual interpretation of numerical data structures in a range of tasks to support decision making that can be solved by software.

Citation: Laptev V.V., Orlov P.A. (2015) Klasternyi analiz vizual’nogo vosprijatija structury dannyh [Cluster analysis of visual perception of data structure]. Business Informatics, no. 3 (33), pp. 34 – 43 (in Russian).
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