Visual Knowledge Discovery in Paleoclimatology with Parallel Coordinates

2006 ◽  
pp. 368-372
Author(s):  
Roberto Therón
Keyword(s):  
Knowledge Discovery ◽  
Parallel Coordinates ◽  
Visual Knowledge

scholarly journals Yewno Discover

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Manisha Bolina
Keyword(s):  
Machine Learning ◽  
Knowledge Discovery ◽  
Computational Linguistics ◽  
Full Text ◽  
Visual Knowledge ◽  
Library Resources ◽  
The Right

Yewno Discover helps students and researchers find precisely the right information they are searching for, regardless of discipline and especially if it is interdisciplinary information. This new kind of visual knowledge discovery tool uses machine learning and computational linguistics to literally read over 200 million full-text  articles and can guide users to the precise paragraph of information within this collection that is most valuable  to their search.  This practical workshop will enable you to learn more how you can use Yewno Discover to engage your patrons to get more of the library resources. Be sure to bring your laptop! 

Visual Knowledge Discovery in Dynamic Enterprise Text Repositories

2009 ◽  
Author(s):  
Vedran Sabol ◽  
Wolfgang Kienreich ◽  
Markus Muhr ◽  
Werner Klieber ◽  
Michael Granitzer
Keyword(s):  
Knowledge Discovery ◽  
Visual Knowledge

Adjustable general line coordinates for visual knowledge discovery in n-D data

2017 ◽  
Vol 18 (1) ◽  
pp. 3-32 ◽  
Author(s):  
Boris Kovalerchuk ◽  
Vladimir Grishin
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Real Data ◽  
Two Dimensions ◽  
Visual Pattern ◽  
Multidimensional Data ◽  
Support Vector ◽  
Parallel Coordinates ◽  
Visual Knowledge ◽  
General Line

Preserving all multidimensional data in two-dimensional visualization is a long-standing problem in Visual Analytics, Machine Learning/Data Mining, and Multiobjective Pareto Optimization. While Parallel and Radial (Star) coordinates preserve all n-D data in two dimensions, they are not sufficient to address visualization challenges of all possible datasets such as occlusion. More such methods are needed. Recently, the concepts of lossless General Line Coordinates that generalize Parallel, Radial, Cartesian, and other coordinates were proposed with initial exploration and application of several subclasses of General Line Coordinates such as Collocated Paired Coordinates and Star Collocated Paired Coordinates. This article explores and enhances benefits of General Line Coordinates. It shows the ways to increase expressiveness of General Line Coordinates including decreasing occlusion and simplifying visual pattern while preserving all n-D data in two dimensions by adjusting General Line Coordinates for given n-D datasets. The adjustments include relocating, rescaling, and other transformations of General Line Coordinates. One of the major sources of benefits of General Line Coordinates relative to Parallel Coordinates is twice less number of point and lines in visual representation of each n-D points. This article demonstrates the benefits of different General Line Coordinates for real data visual analysis such as health monitoring and benchmark Iris data classification compared with results from Parallel Coordinates, Radvis, and Support Vector Machine. The experimental part of the article presents the results of the experiment with about 70 participants on efficiency of visual pattern discovery using Star Collocated Paired Coordinates, Parallel, and Radial Coordinates. It shows advantages of visual discovery of n-D patterns using General Line Coordinates subclass Star Collocated Paired Coordinates with n = 160 dimensions.

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