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A Model for the Progressive Visualization of Multidimensional Data Structure

Updated: Sep 17, 2021

Elio Ventocilla & Maria Riveiro (2020). A Model for the Progressive Visualization

of Multidimensional Data Structure. Conference paper: Cham: Springer, 14th International Joint Conference, VISIGRAPP 2019 Prague, Czech Republic.

This paper presents a model for the progressive visualization and exploration of the structure of large datasets. That is, an abstraction on different components and relations which provide means for constructing a visual representation of a dataset’s structure, with continuous system feedback and enabled user interactions for computational steering, in

spite of size. In this context, the structure of a dataset is regarded as the distance or neighborhood relationships among its data points. Size, on the other hand, is defined in terms of the number of data points. To prove the validity of the model, a proof-of-concept was developed as a Visual Analytics library for Apache Zeppelin and Apache Spark. Moreover, nine user studies where carried in order to assess the usability of the library. The

results from the user studies show that the library is useful for visualizing and understanding the emerging cluster patterns, for identifying relevant features, and for estimating the number of clusters k. Access publication.

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