Some questions I had were:
a) how are the cluster centers and memberships obtained from the
fuzzy-c-means algorithm for the training samples generalised to any
new pattern (test data)? I used a weighted sum of the memberships for each
training pattern based on inverse distance weighing from the test points.
b) has anyone tried using Gustafson-Kessels shape describing covariance
matrices for clustering? how well does it detect the cluster shapes for
different problems?
c) finally, how well can a FCM algorithm be used for problems
for which there are no inherent well separated clusters in the data, but
some additionnal information (such as a set of supervised training data
with a class assigned to each pattern) is available.
Many thanks for your useful help!
Vardhan.
--#-----------------#-----------------#------------------#------------------# Vardhan Walavalkar <----- /| 215, Harvard Ave #3, <----- TWaNg! / | Allston, MA02134. <----- \ | http://crs-www.bu.edu/crs/Vardhan/ <----- \| #-----------------#-----------------#------------------#------------------#
This theory is worthless. It is not even wrong! - Wolfgang Pauli