| 基于MapReduce的并行模糊C均值算法 |
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| 引用本文： | 虞倩倩,戴月明.基于MapReduce的并行模糊C均值算法[J].计算机工程与应用,2013,49(14):133-137. |
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| 作者姓名： | 虞倩倩 戴月明 |
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| 作者单位： | 江南大学 物联网工程学院，江苏 无锡 214122 |
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| 摘 要： | 模糊C均值是一种重要的软聚类算法，针对模糊C均值的随着数据量的增加，时间复杂度过高的缺点，提出了一种基于MapReduce的并行模糊C均值算法。算法重新设计模糊C均值，使其符合MapReduce的基于key/value的编程模型，并行计算数据集到中心点的隶属度，并重新计算出新的聚类中心，提高了模糊C均值处理大容量数据的计算效率。实验结果表明，基于MapReduce的并行模糊C均值算法具有较高的加速比和扩展性。
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| 关 键 词： | 模糊C均值 并行计算 MapReduce编程模型 数据挖掘 云计算 |
Parallel fuzzy C-means algorithm based on MapReduce |
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| YU Qianqian,DAI Yueming.Parallel fuzzy C-means algorithm based on MapReduce[J].Computer Engineering and Applications,2013,49(14):133-137. |
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| Authors: | YU Qianqian DAI Yueming |
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| Affiliation: | School of IOT Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China |
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| Abstract: | Fuzzy C-means?is an important?soft-clustering algorithm, but with the increased amount of data the time complexity will be increased. In this paper, a parallel?fuzzy?C-means?algorithm based on?the MapReduce is proposed. The fuzzy?C-means?algorithm is redesigned to meet the MapReduce programming model. The membership degree of data set to the center is computed in parallel, and the new cluster center is re-calculated, so that the higher calculating efficiency of processing large amount of data can be got. The experimental results show that the parallel?fuzzy?C-means?algorithm based on?the MapReduce has the advantages of both high speedup and good scalability. |
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| Keywords: | fuzzy C-means parallel computing MapReduce data mining cloud computing |
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