We address the problem of the combination of multiple data partitions, that we call a clustering ensemble. We use a recent clustering approach, known as Spectral Clustering, and th...
We propose an unconventional but highly effective approach
to robust fitting of multiple structures by using statistical
learning concepts. We design a novel Mercer kernel
for t...
A pervasive problem in large relational databases is identity uncertainty which occurs when multiple entries in a database refer to the same underlying entity in the world. Relati...
Spectral clustering algorithms have been shown to be more effective in finding clusters than some traditional algorithms such as k-means. However, spectral clustering suffers fro...