Heinz Koeppl, Marc Hafner, et al.
BMC Bioinformatics
The inner product measures how closely two feature vectors are related. It is an important primitive for many popular data mining tasks, for example, clustering, classification, correlation computation, and decision tree construction. If the entire data set is available at a single site, then computing the inner product matrix and identifying the top (in terms of magnitude) entries is trivial. However, in many real-world scenarios, data Is distributed across many locations and transmitting the data to a central server would be quite communication Intensive and not scalable. This paper presents an approximate local algorithm for Identifying top-l Inner products among pairs of feature vectors in a large asynchronous distributed environment such as a peer-to-peer (P2P) network. We develop a probabilistic algorithm for this purpose using order statistics and the Hoeffding bound. We present experimental results to show the effectiveness and scalability of the algorithm. Finally, we demonstrate an application of this technique for Interest-based community formation in a P2P environment. © 2008 IEEE.
Heinz Koeppl, Marc Hafner, et al.
BMC Bioinformatics
Ziyang Liu, Sivaramakrishnan Natarajan, et al.
VLDB
Kafai Lai, Alan E. Rosenbluth, et al.
SPIE Advanced Lithography 2007
Kaoutar El Maghraoui, Gokul Kandiraju, et al.
WOSP/SIPEW 2010