An adaptive parameter space-filling algorithm for highly interactive cluster exploration

Ahmed, Zafar; Weaver, Chris

doi:10.1109/vast.2012.6400493

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…Another example is a system for ecologists, presented in Ahmed and Weaver [1], focusing on large number of data measurements in "development and validation of complex ecological models" ([1], p. 1). In this case the most interesting property is the variation at different time scales.…”

Section: Find Interesting Datamentioning

confidence: 99%

“…In some cases, the interactivity is justified by computational requirements-any visualization that takes too much time and computational power to generate is more or less useless in an interactive setting, thus methods to speed up the operation (through optimization, approximation, or pre-fetching results) fit here. For example, Ahmed and Weaver [1] describes a heuristic pre-fetching method that leads to improved response time of the interactive k-means algorithm in "dynamic query visualizations of multidimensional data" ([1], p. 1). Rasmussen and Karypis [82] provides a clustering platform that allows running multiple clustering algorithms with a work-flow, i.e., import and prepare data, select clustering options, generate reports, and display visualization.…”

Section: No Feedback Reflected To the System To Update Clusteringsmentioning

confidence: 99%

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Interactive Clustering

et al. 2020

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In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.

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Section: Find Interesting Datamentioning

confidence: 99%

Section: No Feedback Reflected To the System To Update Clusteringsmentioning

confidence: 99%

Interactive Clustering

et al. 2020

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“…Such levels of interactivity, of course, require the solutions to be responsive and capable of returning results within acceptable delays. Ahmed and Weaver [AW12] address this problem through forward‐caching expected interaction possibilities and providing users with clustering results without breaking the responsive analytical flow.…”

Section: Categorization Of Machine Learning Techniques Currently Usedmentioning

confidence: 99%

The State of the Art in Integrating Machine Learning into Visual Analytics

Endert

Ribarsky

Turkay

et al. 2017

Computer Graphics Forum

184

122

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Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-ofthe-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

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“…Another example is the iVisClassifier by Choo et al [80] where the authors improve classifitcation performance through interactive visualizations. Ahmed and Weaver [75] discuss how the clustering process can be embedded within an highly interactive system. Examples in biomedical domain are rare in this category.…”

Section: Tight Integrationmentioning

confidence: 99%