We propose a new approach to semi-supervised clustering that utilizes boosting to simultaneously learn both a similarity measure and a clustering of the data from given instancele...
Data intensive applications on clusters often require requests quickly be sent to the node managing the desired data. In many applications, one must look through a sorted tree str...
We propose the hierarchical Dirichlet process (HDP), a nonparametric Bayesian model for clustering problems involving multiple groups of data. Each group of data is modeled with a...
Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, ...
With the continued advancements in location-based services involved infrastructures, large amount of time-based location data are quickly accumulated. Distributed processing techni...
Bin Yang 0002, Qiang Ma, Weining Qian, Aoying Zhou
We propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various cl...
Hye-Sung Yoon, Sang-Ho Lee, Sung-Bum Cho, Ju Han K...