The original VTBeans code was written between 1997 and 1999, and between 1999 and 2004, very few updates have been made (other jobs intervened!). However, I am now in a better position to fix bugs and maintain the code, so bug reports and suggestions would be very welcome, as would any details of contexts where the toolkit has been applied.
Lucy Bastin (04/08/04)
This version of the toolkit runs as a Java application, and can be used with the JDK or JRE. For many purposes, it is therefore more flexible and portable. It also incorporates a number of updates and corrections, and so, unless new users have a particular requirement for the JavaBeans format, they are recommended to download the application version.
To try it out, please download this zip file and follow the instructions.
1. Unzip the file supplied into a new directory (e.g., ' C:\Beans ')
2a - if you have the Java Runtime Environment or Java SDK installed
You can use the 'run.bat' file to start up the application without any edits.
2b - if you don't have the JRE or Java SDK installed
Go to THIS PAGE for some instructions on downloading and installing the JRE
3. Run the application, either with a Windows shortcut to the batch
file, or with the command C:\Beans> run
On running the application, you will see a small pink canvas appear, with two white buttons. To view the data IO and manipulation options (e.g., file reading & writing, data scaling and manipulation), double click on the pink rectangle and select 'Options' To view the Display options, (e.g., maps and animations), use the 'Display' button. The 'Graph' button will bring up the graphing options described in the tutorial.
There are several options in this toolkit which are not described in the existing tutorial - for example, a version of Bezdek's fuzzy c-means classifier algorithm. If you would like more information on these or other aspects of the tookit, please contact l.bastin@aston.ac.uk. I would also be very interested to hear about bugs, problems, uses of the tools or suggestions for additions to the toolkit.
When a quick-view frame is opened by clicking on a layer's symbol, it;
The layer buttons can still be used for a quick grey-scale view of single
layers, but the RGB quick option has been temporarily removed.
The interactive scatterplot now ignores pixels with values of zero or less, making it easier to redraw when datasets are large. The rationale for this change is that when viewing fuzzy memberships, the non-zero memberships only are of interest as far as the coincidence or pulling-apart of cluster centres goes.
Errors in linear scaling of data layers have been corrected.
Data layers can now be normalised to a mean of 0 and standard deviation of 1,
using the ‘Normalise layers’ button, which allows any number of the data layers
to be selected at one time.
A fuzzy C-means cluster algorithm is available for
clustering raster layers or ASCII data files. (Button ‘FCM cluster’). Cluster
number, icon or ‘softness’, norm and maximum iterations are all selected using
dialogs. For each cluster produced, a new data layer will be appended to the
raster data file, with the name ‘FCM0’, ‘FCM1’, etc. This is a direct adaptation
of the Bezdek et al. algorithm from the paper referenced below (incorporating
their own corrections!). For more details on its operation, please see
Bezdek, J.C., Ehrlich, R. & Full, W. (1984)
FCM: The fuzzy c-means clustering algorithm. Computers and Geoscience,
10: 191-203.
For more information on clustering ASCII data files, please contact
l.bastin<at>aston.ac.uk
Updates - 02/08/04
1. Tutorial adapted to describe the application version only, and migrated to Aston University's server. Feedback welcome.