Traditional methods for frequent itemset mining typically assume that data is centralized and static. Such methods impose excessive communication overhead when data is distributed...
Matthew Eric Otey, Chao Wang, Srinivasan Parthasar...
Entities on social systems, such as users on Twitter, and images on Flickr, are at the core of many interesting applications: they can be ranked in search results, recommended to ...
Clustering of data has numerous applications and has been studied extensively. It is very important in Bioinformatics and data mining. Though many parallel algorithms have been des...
In this paper a concern about the accuracy (as a function of parallelism) of a certain class of distributed learning algorithms is raised, and one proposed improvement is illustrat...
Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowye...
To use heterogeneous and geographically distributed resources as a platform for parallel visualization is an intriguing topic of research. This is because of the immense potential...