Paper Info
Title
A Model For The Progressive Visualization Of Multidimensional Data Structure
Abstract
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.
Year
DOI
Venue
2019
10.1007/978-3-030-41590-7_9
COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2019)
Keywords
DocType
Volume
Data structure, Progressive visualization, Large data analysis, Multidimensional projection, Multidimensional data, Growing neural gas, Visual analytics, Exploratory data analysis
Conference
1182
ISSN
Citations 
PageRank 
1865-0929
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Elio Ventocilla100.34
Maria Riveiro200.34