We present a divide-and-merge methodology for clustering a set of objects that combines a top-down "divide" phase with a bottom-up "merge" phase. In contrast, ...
David Cheng, Santosh Vempala, Ravi Kannan, Grant W...
Many real datasets have uncertain categorical attribute values that are only approximately measured or imputed. Uncertainty in categorical data is commonplace in many applications...
At UCLA's Plasma Physics Group, to achieve accessible computational power for our research goals, we developed the tools to build numerically-intensive parallel computing clu...
Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited...
Philipp Kranen, Ira Assent, Corinna Baldauf, Thoma...
This paper investigates topology management of large wireless sensor networks. Due to their random deployment, nodes have to organize themselves as energy efficient as possible to ...
Jakob Salzmann, Ralf Behnke, Jiaxi You, Dirk Timme...