Large margin clustering on uncertain data by considering probability distribution similarity
We study the problem of clustering on uncertain objects. We consider the difference between objects based on probability density functions. We aim at finding the largest margin between clusters to overcome the limitation of UK-means. The experimental results verify the performance of our method by effectiveness, efficiency and scalability on both synthetic and real data sets.
large margin clustering uncertain data probability density function histogram intersection kernel