We present a framework for clustering distributed data in unsupervised and semi-supervised scenarios, taking into account privacy requirements and communication costs. Rather than...
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...
The Lasso is a popular technique for joint estimation and continuous variable selection, especially well-suited for sparse and possibly under-determined linear regression problems....
The least-absolute shrinkage and selection operator (Lasso) is a popular tool for joint estimation and continuous variable selection, especially well-suited for the under-determin...
The emergence of recent XML-based technologies paved the way for new types of architectures and message exchanges on the Internet through Web services. MashUps consist in combinin...
Yosri Harzallah, Vincent Michel, Qi Liu, Gabriel A...