Dietrich Kammer
Mandy Keck
ABSTRACT
With the internet, massively heterogeneous data sources need to be understood and classified to provide suitable services to users such as content observation, data exploration, e-commerce, or adaptive learning environments. The key to providing these services is applying machine learning (ML) in order to generate structures via clustering and classification. Due to the intricate processes involved in ML, visual tools are needed to support designing and evaluating the ML pipelines. In this contribution, we propose a comprehensive tool that facilitates the analysis and design of ML-based clustering algorithms using multiple visualization features such as semantic zoom, glyphs, and histograms.
- Enrico Bertini and Denis Lalanne. 2009. Surveying the Complementary Role of Automatic Data Analysis and Visualization in Knowledge Discovery. In Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration (VAKD '09). ACM, New York, NY, USA, 12--20. Google Scholar
Digital Library
- Jaegul Choo, Hanseung Lee, Zhicheng Liu, John Stasko, and Haesun Park. 2013. An interactive visual testbed system for dimension reduction and clustering of large-scale high-dimensional data. In Visualization and Data Analysis 2013, Vol. 8654. International Society for Optics and Photonics, 865402.Google Scholar
- Florian Heimerl, Steffen Koch, Harald Bosch, and Thomas Ertl. 2012. Visual classifier training for text document retrieval. IEEE Transactions on Visualization and Computer Graphics 18, 12 (2012), 2839--2848. Google ScholarDigital Library
- Mandy Keck, Dietrich Kammer, Thomas Gründer, Thomas Thom, Martin Kleinsteuber, Alexander Maasch, and Rainer Groh. 2017. Towards Glyph-based Visualizations for Big Data Clustering. In Proceedings of the 10th International Symposium on Visual Information Communication and Interaction (VINCI '17). ACM, New York, NY, USA, 129--136. Google ScholarDigital Library
- Daniel A. Keim. 2002. Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics 8, 1 (Jan 2002), 1--8. Google ScholarDigital Library
- Josua Krause, Aritra Dasgupta, Jean-Daniel Fekete, and Enrico Bertini. 2016. SeekAView: An Intelligent Dimensionality Reduction Strategy for Navigating High-Dimensional Data Spaces. Large Data Analysis and Visualization (LDAV), IEEE Symposium on (Oct 2016).Google Scholar
- John Wenskovitch, Ian Crandell, Naren Ramakrishnan, Leanna House, and Chris North. 2018. Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics. IEEE transactions on visualization and computer graphics 24, 1 (2018), 131--141.Google Scholar
Index Terms
- Big data landscapes: improving the visualization of machine learning-based clustering algorithms
Recommendations
Visualizing web search results using glyphs: Design and evaluation of a flower metaphor
While the Web provides a lot of useful information to managers and decision makers in organizations for decision support, it requires a lot of time and cognitive effort for users to sift through a search result list returned by search engines to find ...
Critical design and realization aspects of glyph-based 3D data visualization
SCCG '09: Proceedings of the 25th Spring Conference on Computer GraphicsGlyphs are useful for the effective visualization of multi-variate data. They allow for easily relating multiple data attributes to each other in a coherent visualization approach. While the basic principle of glyph-based visualization has been known ...
Using Visualization to Illustrate Machine Learning Models for Genomic Data
ACSW '19: Proceedings of the Australasian Computer Science Week MulticonferenceMassive amounts of genomic data are created for the advent of Next Generation Sequencing technologies. Visualizing these complex genomic data requires not only simply plotting of data but should also invite a decision or a choice. Machine learning has ...
Comments