DBMD Implementation for Big Data Mining in the CEASE Method

Abstract

The fundamental tools to discover knowledge from big data was matrix composition. Here data generated by modern applications via cloud computing. However, it is still inefficient or infeasible to process very big data using such a method in a single machine or through virtual machines. Moreover, big data are often distributedly collected data from various data centers and stored on different machines via scheduling algorithms. Thus, such data generally bear strong heterogeneous noise. It is essential and useful to develop distributed matrix decomposition for big data analytics. Such a method should scale up well, model the heterogeneous noise, and address the communication issue in a distributed system. To this end, we propose a Distributed Bayesian Matrix Decomposition model (DBMD) for big data mining and clustering. Specifically, we adopt three strategies to implement the distributed computing including (1) the accelerated gradient descent, (2) the alternating direction method of multipliers (ADMM), and (3) the statistical inference. We investigate the theoretical convergence behaviors of these algorithms. To address the heterogeneity of the noise, we propose an optimal plug-in weighted average that reduces the variance of the estimation. Finally, comparison made between these algorithms to understand the result between them.