Parallel Two-Phase K-Means

被引：0

作者：

Cuong Duc Nguyen ^{[1
]}

Dung Tien Nguyen ^{[1
]}

Van-Hau Pham ^{[1
]}

机构：

[1] Int Univ VNU HCM, Hanoi, Vietnam

来源：

COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2013, PT V | 2013年 / 7975卷

关键词：

Data Clustering; K-means; Parallel Distributed Computing; MapReduce; MEANS ALGORITHM;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a new parallel version of Two-Phase K-means, called Parallel Two-Phase K-means (Par2PK-means), is introduced to overcome limits of available parallel versions. Par2PK-means is developed and executed on the MapReduce framework. It is divided into two phases. In the first phase, Mappers independently work on data segments to create an intermediate data. In the second phase, the intermediate data collected from Mappers are clustered by the Reducer to create the final clustering result. Testing on large data sets, the newly proposed algorithm attained a good speedup ratio, closing to the linearly speed-up ratio, when comparing to the sequential version Two-Phase K-means.

引用

页码：224 / 231

页数：8

共 13 条

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