Clustering large datasets with kernel methods


Real-life datasets are becoming larger and less linear separable. Divisive clustering methods with a computation time linear to the number of samples n can handle large data but mostly assume linear boundaries between the cluster in input space. Kernel based clustering methods are able to detect nonlinear boundaries in feature space but have a quadratic… (More)


1 Figure or Table

Slides referencing similar topics