Sammendrag
The focus of this thesis is to develop a Markov Chain based framework
for joint ranking and clustering of a dataset without
the need for critical user-defined hyper-parameters.
Joint ranking and clustering may be useful in several respects,
and may give additional insight for the data analyst,
as opposed to the traditional separate ranking and
clustering procedures.
By coupling Markov chain theory with recent advances in kernel
methods using the so-called probabilistic cluster kernel,
we are able to learn the transition probabilities from the
inherent structures in the data in a near parameter-free approach.
The theory developed in this thesis is applied to several
real world datasets of different types with
promising results.