KMEANSM(1)

NAME
kmeansm - Binary Splitting Kmeans Clusterer

SYNOPSIS
A program for finding cluster centers for the input data using a Euclidian distance to assign points to centers in iterative training of the means.

kmeansm [ -ncenters n ] [ -split_percentage x ]
[ -add_random_offset ] [ -max_iteration n ]
[ -stop_percentage x ] [ -reduce_step n ]
[ -mapper_options ]

EXAMPLE
kmeansm -pathdata $LNKHOME/data/map \
-finput gauss_map.train \
-fdescribe gauss_map.train.defaults -npatterns 200 -nraw 1 \
-noutputs 1

DESCRIPTION
This kmeans mapping program works in the same way as the kmeans program for classification data. The only differences are that this program expects to be given mapping data and that this clusterer cannot do clustering by class.

OPTIONS
The algorithm specific options are those available for the program kmeans with these exceptions

    -cluster_by_class -ncenters_list
        These arguments are unavailable, because there are no classes in mapping data.

-mapper_options
    kmeansm accepts the general mapper arguments described on the mapper(1) manual page rather than the classifier options used by kmeans Certain mapping arguments are not used by the program, and so are not available.

    Unavailable arguments from mapping list:

    -train, -create
        Because training on a model cannot be continued and there is no testing mode, the create and train flags are always set.

REFERENCE
Richard O. Duda and Peter E. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons, New York, 1973. Pages 225-228.

SEE ALSO
mapper(1), kmeans(1)

HISTORY
First written 5 November 1992 by Linda Kukolich

BUGS
Send bug reports to Linda Kukolich (kukolich@sst.ll.mit.edu)