MapReduce框架下的朴素贝叶斯算法并行化研究

研究朴素贝叶斯算法MapReduce的并行实现方法, 针对传统单点串行算法在面对大规模数据或者参与分类的属性较多时效率低甚至无力承载大规模运算, 以及难以满足人们处理海量数据的需求等问题, 本文在朴素贝叶斯基本理论和MapReduce框架的基础上, 提出了一种基于MapReduce的高效、廉价的并行化方法. 通过实验表明这种方法在面对大规模数据时能有效提高算法的效率, 满足人们处理海量数据的需求.

Parallelization of Naive Bayes Algorithm Under MapReduce Framwork

This article focused on the realization of the parallelization of Naive Bayes. When it comes to large-scal data or multi-attributes, the traditional singal node algorithm has a low efficiency,or even is unable to host large-scale computing. All of these make the traditional algorithm cannot fit the need to deal with massive data. Therefore, based on the basic theory of Naive Bayes and the framework of MapReduce, this paper proposed a parallelization method of Naive Bayes, which is efficient and cheap.At the end, it is proved by experiments that this method can effectively improve the efficiency of the algorithm so as to meet the need of peoople to deal with massive data.