Sciweavers

Share
ICDM
2009
IEEE

A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks

9 years 4 months ago
A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks
This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its lo...
Kanishka Bhaduri, Ashok N. Srivastava
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICDM
Authors Kanishka Bhaduri, Ashok N. Srivastava
Comments (0)
books