| 数据划分优化的并行k-means算法 |
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| 引用本文： | 尹建君,王乐.数据划分优化的并行k-means算法[J].计算机工程与应用,2010,46(15):127-131. |
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| 作者姓名： | 尹建君 王乐 |
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| 作者单位： | 1. 成都医学院,人文信息管理学院,成都,610083
2. 国防科技大学,计算机学院,长沙,410073 |
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| 摘 要： | 针对大规模文本聚类中对聚类算法执行效率的要求，提出了一个内容相关的纵向数据划分策略FTDV，并基于该策略提出了数据划分优化的并行DVP k-means算法，提高了常规并行k-means算法的并行化程度，达到了优化算法执行效率的目的。在实验中，与常规并行k-means算法和基于关键方向分解的PDDP k-means算法进行比较，DVP k-means具有更好的并行性和对数据规模的适应性，且可以生成更高质量的聚簇。
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| 关 键 词： | 数据划分 并行聚类算法 频繁词集 k-means算法 |
| 收稿时间： | 2008-11-18 |
| 修稿时间： | 2009-2-23
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Parallel k-means optimized by vertical dataset division |
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| YIN Jian-jun,WANG Le.Parallel k-means optimized by vertical dataset division[J].Computer Engineering and Applications,2010,46(15):127-131. |
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| Authors: | YIN Jian-jun WANG Le |
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| Affiliation: | 1.School of Humanity and Information Management,Chengdu Medical College,Chengdu 610083,China 2.College of Computer,National University of Defense Technology,Changsha 410073,China |
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| Abstract: | For the requirement of high efficiency in large volume of document clustering,this paper proposes a vertical content-related data partition politic,FTVD.A parallel clustering algorithm,called DVP k-means,is proposed based on above FTVD in order to optimize the parallel degree of traditional parallel k-means.Experimental results on two public datasets indicate that DVP k-means performs better than other two parallel algorithms,traditional parallel k-means and PDDP k-means,both on parallelism and feasibility. |
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| Keywords: | data partition parallel clustering algorithm frequent term set k-means |
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