Information Visualization and Visual Data Mining

By Daniel A. Keim. 2002

1,941 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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In Signal Processing

Citations in the corpus

Visualizing Large-scale and High-dimensional Data (2016, background): “ets. Information visualization has been playing a critical role in this pipeline, which facilitates the description, exploration, and sense-making from both the original data and the analysis results [16]. Classical visualization techniques have been proved eective for small or intermediate size dat”
Visualizing High-Dimensional Data: Advances in the Past Decade (2017, neutral): “Detailed surveys and discussions on the topic of visual data mining can be found in [10], [11], [12].”
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, neutral): “Keim proposes an according extension to the information seeking mantra for visual analytics [5]: " Analyze first, Show the Important, Zoom, filter, and analyze further, Details on demand.”
Parallel Sets: interactive exploration and visual analysis of categorical data (2006, methodology): “Using the frequency information means utilizing an aggregation [17] of a large categorical data set, reducing the amount of data to be displayed, and providing an image of the data that more closely resembles the way users think of large, categorical data sets.”
Show Me: Automatic Presentation for Visual Analysis (2007, methodology): “Visual data mining uses statistical algorithms to decide what to show [6].”
Profiler: integrated statistical analysis and visualization for data quality assessment (2012, background): “Visualization can support discovery of patterns in data, including anomalies [18].”
Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines (2010, methodology): “O VERVIEW is one of the most basic user tasks for information visualization [65] and it is a vital building block for the more complex process of visual exploration—using visualization to form, test, and validate hypotheses about complex or large datasets [53].”
Toward a Deeper Understanding of the Role of Interaction in Information Visualization (2007, background): “…overview and context, same representation / changing parameters, same data / changing representation, linking representation – temporal fusion Keim (2002) [24] Dynamic projections, interactive filtering, interactive zooming, interactive distortion, interactive linking and brushing…”
Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation (2008, methodology): “Keim [17] presents a taxonomy of primarily multidimensional visualization techniques, listing categories such as standard 2D/3D displays, geometrically transformed displays [7], iconic displays [6], dense pixel displays [18], and stacked displays [22].”
A Brief Survey of Text Mining (2005, background): “For an overview of these areas of research see, e.g., [UF01, Kei02].”
Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities (2016, methodology): “Several data type taxonomies have been described in literature (Chi, 2000; Keim, 2002; Adnan et al., 2008; Ellis and Dix, 2007).”
Challenges in Visual Data Analysis (2006, background): “Having no possibility of adequately exploring the large amounts of data which have been collected due to their potential usefulness, the data becomes useless and the databases become data “dumps” [2].”
Visualization of Explanations in Recommender Systems (2014, neutral): “Stacked displays include dimensional stacking [17].”