Clustering recipes: new flavours of kernel and spectral methods

4 December 2009
16:30
to
5 December 2009
17:00
Ornella Cominetti
Abstract
<span style="font-style: normal; font-variant: normal; font-weight: normal; font-size: 8px; line-height: normal; font-size-adjust: none; font-stretch: normal; font-family: Helvetica"><span style="font-size: small" class="Apple-style-span"><span class="Apple-style-span" style="font-size: 12px">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) <span class="Apple-style-span" style="font-size: medium"><span style="font-style: normal; font-variant: normal; font-weight: normal; font-size: 8px; line-height: normal; font-size-adjust: none; font-stretch: normal; font-family: Helvetica"><span style="font-size: small" class="Apple-style-span"><span class="Apple-style-span" style="font-size: 12px">for which this method outperforms other frequently used algorithms</span></span></span></span></span></span></span>
  • Junior Applied Mathematics Seminar