一种基于Map Reduce的并行算法

Priyanka Gupta, Vinaya Sawant
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引用次数: 1

摘要

在当今世界,大数据在我们的生活方式中扮演着至关重要的角色。处理大型数据集的快速增长和逻辑算法成为一项具有挑战性的任务。本文的目的是解释使用关联规则挖掘的HadoopMap Reduce框架。ARM最著名的算法之一是Apriori算法,它是用MapReduce人工语言实现的,在Hadoop框架上执行。这些规则是观察总是与数据集一起发生的事实的传统。在使用关联规则时,当数据变得巨大时就会出现问题。为了解决这个问题,他们使用了Hadoop。本文概述了Hadoop - MapReduce框架。除了执行时间和通信开销等问题外,本文最大的挑战是解决可伸缩性问题。本文将解决可靠、可扩展和分布式计算的问题。关键词:数据挖掘,关联规则挖掘,Apriori算法,大数据,Hadoop, MapReduce
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Map Reduce Based Parallel Algorithm
In Today’s world, Big Data became a vital role in our way of life. Growing rapidly and logical algorithms that will handle large datasets becoming a challenging task. This paper aim is to explain the HadoopMap Reduce Framework with Association rule Mining. One of the famous algorithm of ARM is that the Apriori algorithm, which is implemented in MapReduce artificial language, which is executed on the Hadoop framework. These rules are tradition to observe facts that always occur along with datasets. Using association rule, an issue arises when data becomes enormous. To beat this situation they used Hadoop. This paper provides an outline of the Hadoop – MapReduce framework. Our big challenge of this paper are to resolve the matter of scalability, apart from the other problems are execution time and communication overhead. This paper will solve the matter of reliable, scalable and distributed computing. Keywords—Data Mining, Association Rule Mining, Apriori Algorithm, Big Data, Hadoop, MapReduce
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