Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Visualizing Fuzzy Points in Parallel Coordinates

Michael R. Berthold and Lawrence O. Hall

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-99-1082
December 1999

http://www.eecs.berkeley.edu/Pubs/TechRpts/1999/CSD-99-1082.pdf

The ability to visualize data often leads to new insights. Data that is more than three dimensional must be visualized as a series of projections or transformed into some other representation which usually causes a loss of details. Parallel coordinates allows one to visualize data in two dimensions without a loss of information. In this paper, we discuss the use of parallel coordinates to visualize fuzzy data. Fuzzy data may consist of fuzzy rules, which can be viewed as cutting a swath through an n-dimensional space. Fuzzy clusters may also be considered fuzzy data in a similar way. Examples are given from three domains. The examples show that parallel coordinates can be used to find extraneous fuzzy rules, separate fuzzy clusters as well as validate previous findings about data sets.


BibTeX citation:

@techreport{Berthold:CSD-99-1082,
    Author = {Berthold, Michael R. and Hall, Lawrence O.},
    Title = {Visualizing Fuzzy Points in Parallel Coordinates},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {1999},
    Month = {Dec},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/1999/5303.html},
    Number = {UCB/CSD-99-1082},
    Abstract = {The ability to visualize data often leads to new insights. Data that is more than three dimensional must be visualized as a series of projections or transformed into some other representation which usually causes a loss of details. Parallel coordinates allows one to visualize data in two dimensions without a loss of information. In this paper, we discuss the use of parallel coordinates to visualize fuzzy data. Fuzzy data may consist of fuzzy rules, which can be viewed as cutting a swath through an n-dimensional space. Fuzzy clusters may also be considered fuzzy data in a similar way. Examples are given from three domains. The examples show that parallel coordinates can be used to find extraneous fuzzy rules, separate fuzzy clusters as well as validate previous findings about data sets.}
}

EndNote citation:

%0 Report
%A Berthold, Michael R.
%A Hall, Lawrence O.
%T Visualizing Fuzzy Points in Parallel Coordinates
%I EECS Department, University of California, Berkeley
%D 1999
%@ UCB/CSD-99-1082
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/1999/5303.html
%F Berthold:CSD-99-1082