基于Mapreduce模型的并行项贡献算法的设计与实现

2012 7th Open Cirrus Summit Pub Date : 2012-06-19 DOI:10.1109/OCS.2012.39

Peng Chao, Wu Bin, Deng Chao

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引用次数: 3

摘要

MapReduce是谷歌在2004年推出的一个软件框架，用于支持在[1]计算机集群上的大型数据集上进行分布式计算。术语贡献算法(term contribution, TC)是文本挖掘中一种较新的聚类特征选择算法。本文设计并实现了一种基于MapReduce模型的并行项贡献(PTC)算法。通过实验，我们得出结论，使用MapReduce框架大大提高了TC的性能。

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Design and Implementation of Parallel Term Contribution Algorithm Based on Mapreduce Model

MapReduce is a software framework introduced by Google in 2004 to support distributed computing on large datasets on clusters of computers[1]. The term contribution (TC) algorithm is a relatively new algorithm in text mining to select features for clustering. In this paper, we design and implement a parallel term contribution (PTC) algorithm based on MapReduce model. By experiment, we come to the conclusion that the performance of TC is greatly enhanced using MapReduce framework.

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2012 7th Open Cirrus Summit

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