云中的K-means聚类——一个Mahout测试

2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications Pub Date : 2011-03-22 DOI:10.1109/WAINA.2011.136

R. Esteves, Rui Pais, Chunming Rong

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引用次数: 89

摘要

K-Means是一种众所周知的聚类算法，已经成功地应用于各种各样的问题。然而，它的应用通常仅限于小数据集。Mahout是一种运行在Hadoop系统上的K-Means云计算方法。Mahout和Hadoop都是免费开源的。由于其廉价和可扩展的特点，这些平台可以成为解决数据密集型问题的一种很有前途的技术，而这些问题在过去并非微不足道。在这项工作中，我们使用大型数据集研究了Mahout的性能。测试在Amazon EC2实例上运行，并允许比较在多节点集群上运行时的增益。本文介绍了一些正在进行的研究结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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K-means Clustering in the Cloud -- A Mahout Test

The K-Means is a well known clustering algorithm that has been successfully applied to a wide variety of problems. However, its application has usually been restricted to small datasets. Mahout is a cloud computing approach to K-Means that runs on a Hadoop system. Both Mahout and Hadoop are free and open source. Due to their inexpensive and scalable characteristics, these platforms can be a promising technology to solve data intensive problems which were not trivial in the past. In this work we studied the performance of Mahout using a large data set. The tests were running on Amazon EC2 instances and allowed to compare the gain in runtime when running on a multi node cluster. This paper presents some results of ongoing research.

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2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications

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