TITLE:  Clustering by Partial Mixture Modeling

ABSTRACT:  Clustering algorithms based upon some form
of nonparametric or semiparametric density estimate
are of more theoretical interest than some of the
distance-based or ad hoc algorithmic procedures.
However density estimation is subject to the curse of
dimensionality so that care must be exercised.
Clustering algorithms are sometimes described as biased
when solutions are highly influenced by initial configurations.
Mode-finding algorithms are related to but different
than gaussian mixture models.  In this paper,
we describe a hybrid algorithm which finds modes by
fitting incomplete mixture models, or partial mixture
component models.  Bias problems are avoided since
the partial mixture model is fitted many times using
carefully chosen random starting guesses.
The clustering results may be examined using an advanced
visualization tool such as GGobi.  We describe the
algorithms, some simulations, and some real experiments.