itle{ Scale-based Clustering using the Radial Basis Function Net-
work

hanks{The work described here is supported in part by the Nation-
al  Science Foundation under grant ECS-9307632 and in part by ONR
Contract N00014-92C-0232.}}



uthor{Srinivasa V. Chakravarthy and Joydeep Ghosh, \
  Department of Electrical and Computer Engineering,\ The Univer-
sity of Texas,  Austin, TX 78712 \ }


{all bsection*{This paper shows how scale-based clustering can be
done  using the Radial Basis Function (RBF) Network, with the RBF
width as the scale parameter and a dummy target  as  the  desired
output.   The technique suggests the ``right'' scale at which the
given data set should be clustered, thereby providing a  solution
to  the  problem  of  determining the number of RBF units and the
widths required to get a good network solution.  The network com-
pares favorably with other standard techniques on benchmark clus-
tering examples. Properties that  are  required  of  non-gaussian
basis  functions,  if they are to serve in alternative clustering
networks, are identified.
 The work on the whole points out an important role played by the
width  parameter  in RBFN, when observed over several scales, and
provides a fundamental link to the scale space  theory  developed
in computational vision.