
Version 3.5
To whom it may concern:
This directory contains software for doing unsupervised
clustering. This is done by estimating the parameters
of a Gaussian mixture model using the EM algorithm.

Version 3.x - Modified to better handle singular clusters. 
The cluster covariance matrices are regularized by the addition of a small
constant to the diagonal entries.
Version 3.2 - Simplifies the makefile structure and makes
some corrections to the appendix in the manual that
explains the theory of the algorithm.
Version 3.3 - Clearification to manual appendix.
Version 3.4 - Added classify program
Version 3.5 - Cleaned up ANSI C and integrated with SMAP.1.6
Version 3.5.1 - Minor bug fix
Version 3.5.2 - Integrated diagonal option into code, and removed subcluster2.c
Version 3.5.3 - Fixed code so that Rmin is the same for each class
Version 3.5.4 - Fixed bug in unset value of Rmin in seed subroutine


The directory contains the following subdirectories:

documentation/ - This subdirectory contains this manual and other documentation.

src/ - This subdirectory contains the ANSI-C source code and header files 
       required for the ``clust'' program and library. It also contains
       code for the ``classify'' routine that can be used to classify
       data vectors based on the mixture model extracted with ``cluster''.

src/Makefile - This makefile constructs the compiled binaries. These makefiles 
               have been tested for standard Sun and HP unix environments,
               but should work with little or no modification on other 
               platforms supporting ANSI-C code.

example1/ - This subdirectory contains a shell script that runs a simple 
            example showing how the algorithm works.

example2/ - This subdirectory contains a shell script that runs a simple 
            example which uses matlab to construct training data 
            that can be used to model regions of an image.

example3/ - This subdirectory contains a shell script that runs 
            the ``classify'' program. This program shows how
            the mixture models extracted using cluster can be
            used to classify data vectors or compute log likelihoods.


For more information contact:
  Charles A. Bouman
  School of Electrical Engineering
  Purdue University
  West Lafayette IN 47906
  bouman@ecn.purdue.edu
  http://www.ece.purdue.edu/~bouman/

