Clustering recipes: new flavours of kernel and spectral methods
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Fri, 04/12/2009 16:30 |
Ornella Cominetti (University of Oxford) |
Junior Applied Mathematics Seminar  |
DH 3rd floor SR |
| 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 | |||