A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks
This paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P)
environments. The algorithm can be used for a variety of well-known data mining tasks in distributed environments such as clustering, anomaly detection, …