
		GENERAL INFORMATION FOR AUTOCLASS C
-------------------------------------------------------------------------------

CONTENTS:
	What Is Autoclass 
	What Is Autoclass III
	What Is Autoclass X
	What Is Autoclass C
        Update History
	Compatibility & Porting Considerations
        Limitations 
	Building The Autoclass C System
	Use Of The Autoclass C System
	Theoretical Questions
	Technical Questions
	Implementation Questions
	References


WHAT IS AUTOCLASS:

   AutoClass is an unsupervised Bayesian classification system that seeks a
   maximum posterior probability classification.

   Key features:
   - determines the number of classes automatically;
   - can use mixed discrete and real valued data;
   - can handle missing values;
   - processing time is roughly linear in the amount of the data;
   - cases have probabilistic class membership;
   - allows correlation between attributes within a class; 
   - generates reports describing the classes found; and
   - predicts "test" case class memberships from a "training"
     classification.

   Inputs consist of a database of attribute vectors (cases), either real or 
   discrete valued, and a class model.  Default class models are provided.  
   AutoClass finds the set of classes that is maximally probable with respect 
   to the data and model.  The output is a set of class descriptions, and 
   partial membership of the cases in the classes.

   For more details see "Bayesian Classification (AutoClass): Theory and 
   Results" (kdd-95.ps in ~/autoclass-c/doc/), "Bayesian Classification
   Theory" (tr-fia-90-12-7-01.ps in ~/autoclass-c/doc/).  A list of 
   references is included below.


WHAT IS AUTOCLASS III:

   AutoClass III, programmed in Common Lisp, is the official released 
   implementation of AutoClass available from COSMIC (NASA's software 
   distribution agency): 

	COSMIC
	University of Georgia
	382 East Broad Street
	Athens, GA  30602  USA
	voice: (706) 542-3265  fax: (706) 542-4807
	telex: 41- 190 UGA IRC ATHENS
	e-mail:	cosmic@@uga.bitnet  or service@@cossack.cosmic.uga.edu

   Request "AutoClass III - Automatic Class Discovery from Data (ARC-13180)".


WHAT IS AUTOCLASS X:

   AutoClass X is an experimental extension to AutoClass III, available
   only domestically, by means of a non-disclosure agreement.  It
   implements hierarchical classification where attributes are 
   associated with appropriate levels of the class hierarchy.  The 
   search methodology is currently in development.  It is implemented
   in Common Lisp.  Contact Will Taylor (taylor@ptolemy.arc.nasa.gov).


WHAT IS AUTOCLASS C:

   AutoClass C is a publicly available implementation of AutoClass
   III, with some improvements from AutoClass X, done in the C
   language.  It was programmed by Dr. Diane Cook
   (cook@centauri.uta.edu) and Joseph Potts (potts@cse.uta.edu) of
   the University of Texas at Arlington.   Will Taylor
   (taylor@ptolemy.arc.nasa.gov) "productized" the software through
   extensive testing, addition of sample data bases, and re-working
   the user documentation.

   Significant new features of the C implementation are:

   - it is about 10-20 times faster than the Lisp implementations:
      AutoClass III & AutoClass X;
   - it uses double precision floating point for its "inner loop"
     weight calculations, producing a higher "signal-to-noise"
     ratio than the Lisp versions, and thus more precise
     convergences for very large data sets (adding double precision
     to the Lisp versions would slow them down even more).

   It provides four models:

      single_multinomial - discrete attribute multinomial model,
        including missing values.
      single_normal - real valued attribute model with without
        missing values; sub-types: location and scalar.
      single_normal_missing - real valued attribute model with missing 
        values; sub-types: location and scalar.
      multi_normal - real valued covariant normal model without
        missing values.

   Additional models were done in Lisp for AutoClass X, and may be
   implemented in C at some later time.  These models are:

      single_multinomial_ignore - discrete attribute multinomial model,
        ignoring missing values.
      single_poisson - models low value count (integer) attributes 
        as Poisson distributions.
      multi_multinomial_dense - a dense covariant multinomial model.
      multi_multinomial_sparse - a sparse covariant multinomial model.

   The C implementation also does not provide single_multinomial model
   value translations, and canonical model group/attribute ordering.


UPDATE HISTORY:

   Version: 1.0	   15 Apr 95    initial version of AutoClass C

   Version: 1.5	   08 May 95    ported to Sun Solaris 2.4; corrected string
                                overwrite problems; compilation of file
        search-control.c is now optimized; & added binary data file input 
        option.  (See "autoclass-c/version-1-5.text")

   Version: 2.0	   08 Jun 95    ported to SGI IRIX version 5.2; converted
                                binary i/o from non-standard (open/close/
        read/write) to ANSI (fopen/fclose/fread/fwrite); converted from 
        srand/rand to srand48/lrand48 for random number generation; added
        prediction capability which uses a "training" classification to
        predict probabilistic class membership for the cases of a "test"
        data file; added new ".s-params" parameter "screen_output_p"; added
        output of real and discrete attribute statistics when data base is
        initially read; corrected garbage output when ".r-params" parameter
        "xref_class_report_att_list" contains mixed real and discrete
        attributes; corrected the handling of unknown real values in reports
        output; and corrected an error in function "output_warning_msgs"
        which caused an abort condition.  (See "autoclass-c/version-2-0.text")

   Version: 2.5	   28 Jul 95    Influence values report has been
                                significantly revised and reformatted; 
        add SunOS/Solaris C compiler support; correct segmentation fault
        which occurs when more than 25 type = real, subtype = scalar
        attributes are defined; correct "LOG domain" errors in generation
        of influence values for model "single_multinomial"; and added mods 
        for port to Linux operating system using gcc compiler. (See
        "autoclass-c/version-2-5.text") 

   Version: 2.6	   02 Aug 95    Correct segmentation fault which occurs
                                when more than 50 type = real, subtype = 
        scalar attributes are defined; add function safe_log to prevent 
        "log: SING error" error messages; and require user to confirm 
        search runs using test settings for .s-params file parameters: 
        start_fn_type and randomize_random_p.  (See
        "autoclass-c/version-2-6.text") 

   Version: 2.7	   16 Aug 95    Add search parameter to allow AutoClass
                                to be run as a background task.  (See
        "autoclass-c/version-2-7.text") 

   Version: 2.8	   03 Sep 96    Add search parameter "read_compact_p",
        which directs AutoClass to read the "results" and "checkpoint"
        files in either binary format or ascii format; redefine make
        files with -I and -L parameters for SunOS 4.1.3; change make
        file naming conventions; prevent corruption of discrete data 
        translation tables when translations are longer than 40
        characters; increase from 3000 to 20000 the value of 
        VERY_LONG_STRING_LENGTH to handle very large datum lines;
        increase DATA_ALLOC_INCREMENT from 100 to 1000 for reading very
        large datasets; add DATA_ALLOC_INCREMENT logic of READ_DATA
        to XREF_GET_DATA -- this will prevent segmentation faults
        encountered when reading very large .db2 files into the 
        reports processing function of AutoClass; in
        FORMAT_DISCRETE_ATTRIBUTE, do not process attributes with
        warning or error messages -- this prevents segmentation faults;
        in XREF_GET_DATA, free database allocated memory after it is 
        transferred into report data structures --this reduces the
        amount of memory required when generating reports for very
        large data bases, and prevents running out of memory; in all 
        functions calling malloc/realloc for dynamic memory allocation, 
        checks have been added to notify the user if memory is exhausted;
        and port the "make" file for HP-UX operating system using the
        bundled "cc" compiler.  (See "autoclass-c/version-2-8.text") 

   Version: 2.9	   21 Oct 96    Correct bugs which occur when generating
        reports of discrete type data -- these were introduced in version 
        2.8.  Added new parameter for both ".s-params" & ".r-params"
        files: break_on_warnings_p.  (See "autoclass-c/version-2-9.text")

   Version: 3.0	   15 Apr 97    New parameter for .r-params files:
        report_mode -- "text" (current report output) or "data"
        (parsable format for further processing); correct minor bugs;
        improve input checking for .hd2 file; correct segmentation
        fault which occurred in prediction runs when the size of the
        "test" database was larger than that of the "training"
        database; and new parameter for .s-params & .r-params files: 
        free_storage_p.  (See "autoclass-c/version-3-0.text")

   Version: 3.1	   04 Jul 97    New parameters for .r-params files:
        comment_data_headers_p, max_num_xref_class_probs,
        start_sigma_contours_att, & stop_sigma_contours_att.  Allow
        checkpoint files to be loaded for reconvergence.  Allow
        reports to be generated for data sets of 100,000 cases and 
        more, without causing a segmentation fault.  For "-predict"
        runs, handle "test" cases which are not predicted in be in 
        any of the "training" classes.  When there is more than one
        covariant normal correlation matrix, print all of them.
        In the case cross-reference report (report_type = "xref_case")
        generated with the data option (report_mode = "data"), other class
        probabilities are now printed.  In the case and class cross-
        reference reports, the print out of probabilities has increased
        by one significant digit (0.04 => 0.041), and the minimum value 
        printed is now 0.001, rather than 0.01.  Add capability to
        compute sigma class contour values for specified pairs of 
        real valued attributes.  (See "autoclass-c/version-3-1.text")

   Version: 3.2    13 Apr 98    Changed the behavior of search
        parameter force_new_search_p; amplified some documentation
        sections; corrected several segmentation faults in reports
        generation; corrected several errors in sigma contours output;
        correct problem with cross-reference reports class assignment
        when there are more than five marginal probabilities; change
        layout of influence values report to print matrices after all 
        class attributes are listed; warn user when default start_j_list 
        may not find the correct number of classes in data set; warn 
        user of search trials which do not converge and print 
        convergence summary at the end of each run; the multi-normal 
        model was corrected to prevent oscillation in the expectation 
        maximization calculations; and allow non-contiguous groups of 
        attributes to be specified for sigma contours calculations.
        (See "autoclass-c/version-3-2.text") 

   Version: 3.2.1  04 Jun 98    Minor documentation changes.  (See
        "autoclass-c/version-3-2-1.text")

   Version: 3.2.2  02 Jul 98    Minor documentation changes.  (See
        "autoclass-c/version-3-2-2.text")

   Version: 3.3    23 Sep 98    Integrated source port of version
        3.2.2 to Windows NT/95. Update sample AutoClass C run files
        contained in autoclass-c/sample. (See
        "autoclass-c/version-3-3.text")

   Version: 3.3.1  30 Nov 98    Correct incompatibility with
        .results[-bin] files written by AutoClass C versions prior 
        to version 3.3.  (See "autoclass-c/version-3-3-1.text")

   Version: 3.3.2  13 Sep 99    In all situations warning and error 
        messages are now written to the log file.  (See
        "autoclass-c/version-3-3-2.text")


COMPATIBILITY & PORTING CONSIDERATIONS:

   AutoClass C was written in ANSI C using the GNU gcc compiler
   version 2.6.3 running on a SunSparc under SunOS 4.1.3.

   It has also been ported to and tested on:
     - SunSparc under Solaris 2.4 using SPARCompiler C version 3.00;
     - SunSparc under SunOS 4.1.3 using SPARCompiler C version 3.00;
     - SGI Indigo under IRIX 5.2 using the bundled cc compiler;
     - Linux version 1.2.10, GCC version 2.5.8, libc version
           4.6.25;
     - HP9000/735 & HP9000/C110 under HPUX 10.10 using the bundled
           cc compiler;
     - Windows NT/95 using the Microsoft Visual C++ 5.0 compiler.

   Considerations for porting to other platforms, operating systems,
   and compilers:

   - int & float types must be at least 32 bit words
   - floating point arithmetic must be IEEE standard
   - values.h constant #defines are not consistent with IEEE standard --
        used Symbolics Genera 8.3 values in autoclass.h
   - globals.c, io-results.c, & search-control-2.c:
        G_safe_file_writing_p = TRUE; only supported under Unix, 
        since it does system calls to mv (rename file) and rm (delete
        file).
   - utils.c: char_input_test -- which implements the typing of 'q'
        and <return> to quit the search --  uses Unix system call fcntl,
        and file fcntlcom-ac.h; get_universal_time -- uses Unix system
        call time.
   - init.c: init -- uses Unix system call getcwd (get current working
        directory); sets "normalizer" value for random number generator 
        library function "srand48".
   - search-control.c, search-basic.c, search-control-2.c, & utils.c:
        Use C library functions srand48/lrand48 for random number
        generation.


LIMITATIONS:

   AutoClass C is limited by memory requirements that are roughly in 
   proportion to the number of data, times the number of attributes (the 
   data space); plus the number of classes, times number of modeled 
   attributes (the model space); plus a fixed program space.  Thus there 
   should be no limit on the number of attributes beyond the program 
   addressable memory, but there are definite tradeoffs with respect to
   the model space, and performance degradations as paging requirements 
   increase.

   For very large data sets, you may well find that even if you can handle
   the data, the processing time is excessive.  If that is the case, it may 
   be worthwhile to try class generation on random subsets of the data set.  
   This should pick out the major classes, although it will miss small 
   ones that are only vaguely represented in the random subsets. You can 
   then switch to prediction mode to classify the entire data set.


BUILDING THE AUTOCLASS C SYSTEM -- UNIX PLATFORMS

    Assuming that "." is not in $PATH --

    % cd ~/autoclass-c 		# or equivalent
    % chmod u+x load-ac         # if you have not already done so
    % load-ac

    { Which compiler, GNU(gcc) or SunOS(acc)? - {gcc|acc}: }
    { Which compiler, GNU(gcc) or Solaris(cc)? - {gcc|cc}: }
    { no prompt if SGI or Linux }

    <compiler and linker messages>

    % ./autoclass-c/autoclass    # show autoclass options

    AutoClass Search: 
      % ./autoclass -search <.db2[-bin] file path> <.hd2 file path>
             <.model file path> <.s-params file path> 

    AutoClass Reports: 
      % ./autoclass -reports <.results[-bin] file path> <.search file path> 
             <.r-params file path> 

    AutoClass Prediction: 
      % ./autoclass -predict <test.. .db2 file path>
             <training.. .results[-bin] file path>
             <training.. .search file path> <training.. .r-params file path> 


BUILDING THE AUTOCLASS C SYSTEM -- WINDOWS PLATFORMS

    Use Mirosoft Visual C++ 5.0 Developer Studio to build Autoclass.exe
        File->Open Workspace: f:\autoclass-c-win\prog\AutoclassC.dsw
        Build->Build Autoclass.exe

    f:\autoclass-c-win> copy prog\Debug\Autoclass.exe .

    f:\autoclass-c-win> Autoclass.exe    # show autoclass options

    AutoClass Search: 
      f:\autoclass-c-win> Autoclass.exe -search <.db2[-bin] file path> <.hd2 file path>
             <.model file path> <.s-params file path> 

    AutoClass Reports: 
      f:\autoclass-c-win> Autoclass.exe -reports <.results[-bin] file path>
             <.search file path> <.r-params file path> 

    AutoClass Prediction: 
      f:\autoclass-c-win> Autoclass.exe -predict <test.. .db2 file path>
             <training.. .results[-bin] file path>
             <training.. .search file path> <training.. .r-params file path> 


USE OF THE AUTOCLASS C SYSTEM -- UNIX PLATFORMS 

   Assuming that "." is not in $PATH --

   To use Autoclass, first you need data (your ".db2" file), then you need to
   describe it to AutoClass (your ".hd2" & ".model" files), and also tell
   AutoClass what parameter values to use for the search (your ".s-params"
   file) and for the report generation (your ".r-params" file).  Next, you
   generate classification results from your data using

        % cd ~/autoclass-c
        % ./autoclass-c/autoclass -search data/glass/glassc.db2  
                data/glass/glass-3c.hd2 data/glass/glass-mnc.model 
                data/glass/glassc.s-params

   and you produce reports with

        % ./autoclass-c/autoclass -reports data/glass/glassc.results-bin
                data/glass/glassc.search data/glass/glassc.r-params

   and, optionally, use this classification for prediction of test cases

        % ./autoclass-c/autoclass -predict data/glass/glassc-predict.db2
                data/glass/glassc.results-bin
                data/glass/glassc.search data/glass/glassc.r-params

   See autoclass-c/doc/introduction-c.text for detailed documentation of the 
   AutoClass C system.

   A database with sample classification run output is provided in
   ~/autoclass-c/sample/.

   Test databases, with .db2, .hd2, .model, .s-params, and .r-params
   files for each of the model term types, are provided in:
        ~/autoclass-c/data/autos/
        ~/autoclass-c/data/3-dim/
        ~/autoclass-c/data/glass/
        ~/autoclass-c/data/rna/
        ~/autoclass-c/data/soybean/

   Test summary output for these databases is provided in:
        ~/autoclass-c/data/tests.c
   Note that the parameters specified in the .s-params files for the
   test data bases specify repeatable, non-random classification
   runs.  For proper random classifications of your data sets,
   remove these "override" parameters in your .s-params files.


USE OF THE AUTOCLASS C SYSTEM -- WINDOWS PLATFORMS 

   To use Autoclass, first you need data (your ".db2" file), then you need to
   describe it to AutoClass (your ".hd2" & ".model" files), and also tell
   AutoClass what parameter values to use for the search (your ".s-params"
   file) and for the report generation (your ".r-params" file).  Next, you
   generate classification results from your data using

        > cd f:\autoclass-c-win             # for example
        f:\autoclass-c-win> Autoclass.exe -search data\glass\glassc.db2  
                data\glass\glass-3c.hd2 data\glass\glass-mnc.model 
                data\glass\glassc.s-params

   and you produce reports with

        f:\autoclass-c-win> Autoclass.exe -reports data\glass\glassc.results-bin
                data\glass\glassc.search data\glass\glassc.r-params

   and, optionally, use this classification for prediction of test cases

        f:\autoclass-c-win> Autoclass.exe -predict data\glass\glassc-predict.db2
                data\glass\glassc.results-bin
                data\glass\glassc.search data\glass\glassc.r-params

   See autoclass-c-win\doc\introduction-c.text for detailed documentation of the 
   AutoClass C system.

   A database with sample classification run output is provided in
   f:\autoclass-c-win\sample\.

   Test databases, with .db2, .hd2, .model, .s-params, and .r-params
   files for each of the model term types, are provided in:
        f:\autoclass-c-win\data\autos\
        f:\autoclass-c-win\data\3-dim\
        f:\autoclass-c-win\data\glass\
        f:\autoclass-c-win\data\rna\
        f:\autoclass-c-win\data\soybean\

   Test summary output for these databases is provided in:
        f:\autoclass-c-win\data\tests.c
   Note that the parameters specified in the .s-params files for the
   test data bases specify repeatable, non-random classification
   runs.  For proper random classifications of your data sets,
   remove these "override" parameters in your .s-params files.


THEORETICAL QUESTIONS:
 
   Contact Peter Cheeseman (cheesem@ptolemy.arc.nasa.gov) if you have 
   questions concerning the theoretical aspects of AutoClass.


TECHNICAL QUESTIONS:
 
   Contact John Stutz (stutz@ptolemy.arc.nasa.gov) if you have questions 
   concerning the applicability of AutoClass to your data analysis
   situation.


IMPLEMENTATION QUESTIONS:
 
   Contact Will Taylor (taylor@ptolemy.arc.nasa.gov) if you have questions 
   concerning the implementation, installation, and running of AutoClass C, 
   including "bugs" and features you may add to the existing code.


REFERENCES:

P. Cheeseman, et al. "Autoclass: A Bayesian Classification System",
  Proceedings of the Fifth International Conference on Machine Learning, 
  Ann Arbor, MI. June 12-14 1988.  Morgan Kaufmann, San Francisco, 1988,
  pp. 54-64, 

P. Cheeseman, et al. "Bayesian Classification", Proceedings of the 
  Seventh National Conference of Artificial Intelligence (AAAI-88), 
  St. Paul, MN. August 22-26, 1988.  Morgan Kaufmann, San Francisco, 
  1988, pp. 607-611.
J. Goebel, et al. "A Bayesian Classification of the IRAS LRS Atlas",
  Astron. Astrophys. 222, L5-L8 (1989).

P. Cheeseman, et al. "Automatic Classification of Spectra from the Infrared
  Astronomical Satellite (IRAS)", NASA Reference Publication 1217 (1989)

P. Cheeseman, "On Finding the Most Probable Model", Computational Models
  of Discovery and Theory Formation, ed. by Jeff Shrager and Pat Langley.
  Morgan Kaufmann, San Francisco, 1990, pp. 73-96.

R. Hanson, J. Stutz, P. Cheeseman, "Bayesian Classification Theory",
  Technical Report FIA-90-12-7-01, NASA Ames Research Center, Artificial 
  Intelligence Branch, May 1991

R. Hanson, J. Stutz, P. Cheeseman, "Bayesian Classification with 
  Correlation and Inheritance", Proceedings of 12th International Joint 
  Conference on Artificial Intelligence, Sydney, Australia. August 24-30,
  1991.  Morgan Kaufmann Publishers, San Francisco, 1991, pp.692-698.

B. Kanefsky, J. Stutz, P. Cheeseman, "An Automatic Classification of a
  Landsat/TM Image from Kansas (FIFE)", Technical Report FIA-91-26,
  NASA Ames Research Center, Artificial Intelligence Branch, September 1991.

B. Kanefsky, J. Stutz, P. Cheeseman, W. Taylor, "An Improved Automatic
  Classification of a Landsat/TM Image from Kansas (FIFE)", Technical 
  Report FIA-94-01, NASA Ames Research Center, Artificial Intelligence
  Branch, January 1994.

J. Stutz, P. Cheeseman, "AutoClass - a Bayesian Approach to Classification",
  in "Maximum Entropy and Bayesian Methods, Cambridge 1994", John Skilling
  & Subuiso Sibisi Eds. Kluwer Academic Publishers, Dordrecht, 1995.

P. Cheeseman, J. Stutz, "Bayesian Classification (AutoClass): Theory and
  Results", in Advances in Knowledge Discovery and Data Mining, 
  Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, & Ramasamy
  Uthurusamy, Eds. The AAAI Press, Menlo Park, expected fall 1995.
