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Timestamp:
02/27/13 15:02:50 (14 months ago)
Author:
Ales Erjavec <ales.erjavec@…>
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default
Message:

Cleanup of 'Widget catalog' documentation.

Fixed rst text formating, replaced dead hardcoded reference links (now using
:ref:), etc.

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1 edited

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  • docs/widgets/rst/visualize/radviz.rst

    r11050 r11359  
    66.. image:: ../icons/Radviz.png 
    77 
    8 Radviz vizualization with explorative data analysis and intelligent data visualization enhancements. 
     8Radviz vizualization with explorative data analysis and intelligent data 
     9visualization enhancements. 
    910 
    1011Signals 
     
    3233----------- 
    3334 
    34 Radviz (Hoffman et al., 1997) is a neat non-linear multi-dimensional visualization technique that can display data on three or more attributes in a 2-dimensional projection. 
    35 The visualized attributes are presented as anchor points equally spaced around the perimeter of a unit circle. Data instances are shown as points inside the circle, with their positions determined by a 
    36 metaphor from physics: each point is held in place with springs that are attached at the other end to the attribute anchors. The stiffness of each spring is proportional to the value of the corresponding attribute and the point ends up at the position where the spring forces are in equilibrium. Prior to visualization, attribute values are scaled to lie between 0 and 1. Data instances that are close to a set of feature anchors have higher values for these features than for the others. 
     35Radviz ([Hoffman1997]_) is a neat non-linear multi-dimensional visualization 
     36technique that can display data on three or more attributes in a 2-dimensional 
     37projection. The visualized attributes are presented as anchor points equally 
     38spaced around the perimeter of a unit circle. Data instances are shown as 
     39points inside the circle, with their positions determined by a metaphor from 
     40physics: each point is held in place with springs that are attached at the 
     41other end to the attribute anchors. The stiffness of each spring is 
     42proportional to the value of the corresponding attribute and the point ends up 
     43at the position where the spring forces are in equilibrium. Prior to 
     44visualization, attribute values are scaled to lie between 0 and 1. Data 
     45instances that are close to a set of feature anchors have higher values for 
     46these features than for the others. 
    3747 
    38 The snapshot shown below shows a Radviz widget with a visualization of the data set from functional genomics (Brown et al.). In this particular visualization the data instances are colored according to the corresponding class, and the visualization space is colored according to the computed class probability. Notice that the particular visualization very nicely separates the data instances of the different class, making the   visualization interesting and potentially informative. 
     48The snapshot shown below shows a Radviz widget with a visualization of the 
     49data set from functional genomics ([Brown2000]_). In this particular 
     50visualization the data instances are colored according to the corresponding 
     51class, and the visualization space is colored according to the computed class 
     52probability. Notice that the particular visualization very nicely separates 
     53the data instances of the different class, making the visualization interesting 
     54and potentially informative. 
    3955 
    4056.. image:: images/Radviz-Brown.png 
    4157 
    42 To gain further understanding about the placement of the data points in two-dimensional space, it helps to set on the :obj:`Show value lines` and use :obj:`Tooltips show spring values`. We also switched-off the :obj:`Show probabilities` to see the markings associated with data points better. The resulting display is shown below. From it, it should be clear that high values of "spo5 11" attribute (and for some data instances high values of "spo mid") is quite characteristic for instance of class Ribo, which at the same time have comparable lower value of other attributes. High values of heat 20 and diau f are characteristic fir Resp class. See Leban et al. (2006) and Mramor et al. (2007) for further illustrations of utility of Radviz in analysis of this and similar data set from functional genomics. Other options in the :obj:`Settings` tab are quite standard. The :obj:`Point size` controls the size of the points that mark the data instnace. :obj:`Jittering Options` are especially interesting when displaying data with discrete attributes, where many of the data instances would overlap. Same could happen also with continuous attributes if many data instances use the same value of the attributes. :obj:`Scaling Options` can shrink or blow-up the visualization from its central point. From :obj:`General Graph Settings`, which mainly includes standard point-visualization options, let us bring to your attention :obj:`Show value lines` which we used in the visualization below and which tells the widget to annotate each data point with a set of lines, each corresponding with each of the attributes displayed. The length of these lines are proportional to the attribute values (no line if the value is minimal). A slider accompanying this option sets the scale in which the lines are drawn. :obj:`Tooltip Settings` determine which information is being displayed when the pointer gets over the data instance. 
     58To gain further understanding about the placement of the data points in 
     59two-dimensional space, it helps to set on the :obj:`Show value lines` and 
     60use :obj:`Tooltips show spring values`. We also switched-off the 
     61:obj:`Show probabilities` to see the markings associated with data points 
     62better. The resulting display is shown below. From it, it should be clear that 
     63high values of "spo5 11" attribute (and for some data instances high values of 
     64"spo mid") is quite characteristic for instance of class Ribo, which at the 
     65same time have comparable lower value of other attributes. High values of 
     66heat 20 and diau f are characteristic for Resp class. See [Leban2006]_ and 
     67[Mramor2007]_ for further illustrations of utility of Radviz in analysis of 
     68this and similar data set from functional genomics. Other options in the 
     69:obj:`Settings` tab are quite standard. The :obj:`Point size` controls the size 
     70of the points that mark the data instnace. :obj:`Jittering Options` are 
     71especially interesting when displaying data with discrete attributes, where 
     72many of the data instances would overlap. Same could happen also with 
     73continuous attributes if many data instances use the same value of the 
     74attributes. :obj:`Scaling Options` can shrink or blow-up the visualization from 
     75its central point. From :obj:`General Graph Settings`, which mainly includes 
     76standard point-visualization options, let us bring to your attention 
     77:obj:`Show value lines` which we used in the visualization below and which 
     78tells the widget to annotate each data point with a set of lines, each 
     79corresponding with each of the attributes displayed. The length of these lines 
     80are proportional to the attribute values (no line if the value is minimal). 
     81A slider accompanying this option sets the scale in which the lines are drawn. 
     82:obj:`Tooltip Settings` determine which information is being displayed when the 
     83pointer gets over the data instance. 
    4384 
    4485.. image:: images/Radviz-Brown-Springs.png 
    4586 
    46 Just like all point-based visualizations, this widget includes tools for intelligent data visualization (VizRank and FreeViz, see Leban et al. (2006) and <a href="">Demsar et al. (2007)</a>) and interface for explorative data analysis - selection of data points in visualization. Just like in `Scatterplot widget <Scatterplot.htm>`_, intelligent visualization can be used to find a set of attributes that would result in an interesting visualization. For now, this works only with class-labeled data set, where interesting visualizations are those that well separate data instances of different class. Radviz graph above is according to this definition an example of a very good visualization, while the one below - where we show an VizRank's interface (:obj:`VizRank` button in :obj:`Optimization dialogs`) with a list of 5-attribute visualizations and their scores - is not. See documentation of `Scatterplot widget <Scatterplot.htm>`_ for further details on VizRank, and for description of explorative analysis functions (selection of data instances and zooming). 
     87Just like all point-based visualizations, this widget includes tools for 
     88intelligent data visualization (VizRank and FreeViz, see [Leban2006]_) and 
     89[Demsar2007]_) and interface for explorative data analysis - selection of data 
     90points in visualization. Just like in :ref:`Scatter Plot` widget, intelligent 
     91visualization can be used to find a set of attributes that would result in an 
     92interesting visualization. For now, this works only with class-labeled data 
     93set, where interesting visualizations are those that well separate data 
     94instances of different class. Radviz graph above is according to this 
     95definition an example of a very good visualization, while the one below - where 
     96we show an VizRank's interface (:obj:`VizRank` button in 
     97:obj:`Optimization dialogs`) with a list of 5-attribute visualizations and 
     98their scores - is not. See documentation of :ref:`Scatter Plot` widget for 
     99further details on VizRank, and for description of explorative analysis 
     100functions (selection of data instances and zooming). 
    47101 
    48102References 
    49103---------- 
    50104 
    51    - Hoffman,P.E. et al. (1997) DNA visual and analytic data mining. In the Proceedings of the IEEE Visualization. Phoenix, AZ, pp. 437-441. 
    52    - Brown, M. P., W. N. Grundy, et al. (2000). "Knowledge-based analysis of microarray gene expression data by using support vector machines." Proc Natl Acad Sci U S A 97(1): 262-7. 
    53    - Leban, G., B. Zupan, et al. (2006). "VizRank: Data Visualization Guided by Machine Learning." Data Mining and Knowledge Discovery 13(2): 119-136. 
    54    - Demsar J, Leban G, Zupan B. FreeViz-An intelligent multivariate visualization approach to explorative analysis of biomedical data. J Biomed Inform 40(6):661-71, 2007. 
    55    - Mramor M, Leban G, Demsar J, Zupan B. Visualization-based cancer microarray data classification analysis. Bioinformatics 23(16): 2147-2154, 2007. 
     105.. [Hoffman1997] Hoffman,P.E. et al. (1997) DNA visual and analytic data mining. 
     106   In the Proceedings of the IEEE Visualization. Phoenix, AZ, pp. 437-441. 
     107 
     108.. [Brown2000] Brown, M. P., W. N. Grundy, et al. (2000). 
     109   "Knowledge-based analysis of microarray gene expression data by using 
     110   support vector machines." Proc Natl Acad Sci U S A 97(1): 262-7. 
     111 
     112.. [Leban2006] Leban, G., B. Zupan, et al. (2006). "VizRank: Data Visualization 
     113   Guided by Machine Learning." Data Mining and Knowledge Discovery 13(2): 
     114   119-136. 
     115 
     116.. [Demsar2007] Demsar J, Leban G, Zupan B. FreeViz-An intelligent multivariate 
     117   visualization approach to explorative analysis of biomedical data. J Biomed 
     118   Inform 40(6):661-71, 2007. 
     119 
     120.. [Mramor2007] Mramor M, Leban G, Demsar J, Zupan B. Visualization-based 
     121   cancer microarray data classification analysis. Bioinformatics 23(16): 
     122   2147-2154, 2007. 
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