| Binary-Positive下的并行化CURE算法 |
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| 引用本文： | 王 民,尹 超,王稚慧,要趁红,高 婧.Binary-Positive下的并行化CURE算法[J].计算机工程与应用,2014(11):58-61. |
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| 作者姓名： | 王 民 尹 超 王稚慧 要趁红 高 婧 |
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| 作者单位： | 西安建筑科技大学信息与控制工程学院,西安710055 |
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| 基金项目： | 国家自然科学基金（No.61073196）;陕西省教育厅专项科研计划项目基金（No.11JK0982）. |
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| 摘 要： | 当CURE算法在处理不均匀的海量数据时,针对随机抽样不具有代表性的问题,提出了一种健壮的并行化改进算法。该算法使用Binary-Positive算法得到原始数据的有效属性,并利用MapReduce并行框架对有效数据进行层次聚类,从而实现了正确率与效率的一种权衡。实验分析表明,改进后的CURE算法具有更高的执行效率,且聚类效果良好。
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| 关 键 词： | 聚类 利用代表点聚类(CURE) Binary-Positive MapReduce 并行 |
Parallel CURE algorithm with Binary-Positive |
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| WANG Min,YIN Chao,WANG Zhihui,YAO Chenhong,GAO Jing.Parallel CURE algorithm with Binary-Positive[J].Computer Engineering and Applications,2014(11):58-61. |
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| Authors: | WANG Min YIN Chao WANG Zhihui YAO Chenhong GAO Jing |
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| Affiliation: | ( School of Information and Control Engineering, Xi＇an University of Architecture and Technology, Xi＇an 710055, China) |
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| Abstract: | For random sampling is not representative, it proposes a robust parallel improvement of algorithms when using CURE algorithm to handle non-uniform mass data. It uses the Binary-Positive algorithm to get the effective properties of the data, uses valid data for hierarchical clustering with MapReduce, which is a distributed parallel framework. It achieves the correct rate and efficiency of a trade-off. The tests show that the improved CURE algorithm has a higher efficiency in the implementation and has a good clustering result. |
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| Keywords: | clustering Clustering Using Representative（CURE） Binary-Positive MapReduce parallel |
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