last updated January 12, 1999
Click on "focus," then set the class breaks of the second class so that it contains only the value(s) of interest of the color variable.  In this example, the color variable is the class returned by the data mining algorithm, and all observations falling in class 6 are focused.
 
 
 
 
 
 
 
 

 

 

 

 

 

Focusing the PCP 

A researcher may isolate observations which share a value or a range of values of a particular variable.  Focusing the PCP removes the lines of all other observations from the display to reduce the visual clutter and draws the focused observations in yellow.  Questions that might be answered easily using this technique might be "are the outliers of the barometric pressure variable also extreme values of other variables, like precipitation?"  or  "how similar are the traces of all of the observations at this latitude?" or, in a data mining context, "what variable dominates the classification of the observations in class #6?"  Using focusing to answer this last question is illustrated at left.  The color variable is set to class#, and the range of values for focus is set to 5.4444 to 6.3.  Since the classes are output in integers, this will isolate only those observations classed by AutoClass into class #6.  Upon clicking apply, all observations other than those in class #6 are removed, and the multivariate relationships of the class #6 observations, recolored yellow, can be isolated and examined. 

To remove the focus, choose one of the color scheme radio buttons. 

In the larger Apoala visualization system, this operation is linked with other representation forms; that is, performing this operation on the PCP will alter maps, scatter plots, and other representation forms in a similar way. 

Go to the sample plot. 

Go to the users guide home page. 

Go to the web version of the paper.

text and graphics by Rob Edsall, web design by Mark Harrower
©1999 The Pennsylvania State University