Title :
A fault tolerant peer-to-peer distributed EM algorithm
Author :
Safarinejadian, Behrooz ; Menhaj, Mohammad B. ; Karrari, Mehdi
Author_Institution :
Electr. Eng. Dept., Amirkabir Univ. of Technol., Tehran
fDate :
March 30 2009-April 2 2009
Abstract :
In this paper, a distributed Expectation Maximization (EM) algorithm is proposed for estimating parameters of a Gaussian mixture model in a peer-to-peer network. This algorithm is used for density estimation and clustering of data distributed over nodes of a network. Scalability and fault tolerance are two important advantages of this method. In the E-step of this algorithm, each node calculates local sufficient statistics using its local observations. A peer-to-peer algorithm is then used to diffuse local sufficient statistics to neighboring nodes and estimate global sufficient statistics in each node. In the M-step, each node updates parameters of the Gaussian mixture model using the estimated global sufficient statistics. The proposed method is then used for environmental monitoring and also distributed target classification. Simulation results approve promising performance of this algorithm.
Keywords :
Gaussian processes; fault tolerance; optimisation; pattern classification; pattern clustering; peer-to-peer computing; statistical analysis; Gaussian mixture model; density estimation; distributed expectation maximization algorithm; distributed target classification; fault tolerant peer-to-peer computing; local sufficient statistic; pattern clustering; Association rules; Clustering algorithms; Data mining; Distributed databases; Fault tolerance; Gaussian distribution; Gaussian processes; Parameter estimation; Peer to peer computing; Statistical distributions;
Conference_Titel :
Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2765-9
DOI :
10.1109/CIDM.2009.4938628
