Seminar series
Date
Fri, 04 Dec 2009
16:30 -
Sat, 05 Dec 2009
17:00
Location
DH 3rd floor SR
Speaker
Ornella Cominetti
Organisation
University of Oxford
Soft
(fuzzy) clustering techniques are often used in the study of
high-dimensional datasets, such as microarray and other high-throughput
bioinformatics data. The most widely used method is Fuzzy C-means
algorithm (FCM), but it can present difficulties when dealing with
nonlinear clusters. In this talk, we will overview and compare
different clustering methods. We will introduce DifFUZZY, a novel
spectral fuzzy clustering algorithm applicable to a larger class of
clustering problems than FCM. This method is better at handling
datasets that are curved, elongated or those which contain clusters of
different dispersion. We will present examples of datasets (synthetic
and real) for which this method outperforms other frequently used algorithms