An Experimental Comparison of Kernel Clustering Methods
Maurizio Filippone, Francesco Masulli, Stefano Rovetta · Frontiers in artificial intelligence and applications · 2009
In this paper, we compare the performances of some among the most popular kernel clustering methods on several data sets. The methods are all based on central clustering and incorporate in various ways the concepts of fuzzy clustering and kernel machines. The data sets are a sample of several application domains and sizes. A thorough discussion about the techniques for validating results is also presented. Results indicate that clustering in kernel space generally outperforms standard clustering, although no method can be proven to be consistently better than the others.