| 一种应用于噪声点分布密集环境下的噪声点识别算法 |
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| 引用本文： | 陈平华,周鹏.一种应用于噪声点分布密集环境下的噪声点识别算法[J].广东工学院学报,2014(3):39-43. |
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| 作者姓名： | 陈平华 周鹏 |
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| 作者单位： | 广东工业大学计算机学院,广东广州510006 |
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| 基金项目： | 广东省教育部产学研结合项目（2012B091100003,2012B091000058） |
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| 摘 要： | 为了提高DBSCAN及其改进算法在噪声点分布密集环境下的噪声点识别率，通过结合PageRank算法思想及噪声数据分布密集的特点，构造簇间投票映射函数，提出了簇间投票噪声点识别算法-NoiseRank 。实验结果表明，在噪声点分布密集环境下，NoiseRank算法比DBSCAN算法具有更高的噪声点识别率。
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| 关 键 词： | 噪声点识别 噪声点分布密集 簇间投票 DBSCAN PageRank |
A Recognition Algorithm of Noise Applied to Environments with Intensive Noise-data Distribution |
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| Authors: | Chen Ping-hua Zhou Peng |
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| Affiliation: | (School of Computers, Guangdong University of Technology, Guangzhou 510006,China) |
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| Abstract: | By combining the PageRank algorithm with the features of intensive noise-data to improve the noise-data recognition rate of DBSCAN in environments with intensive Noise-Point distribution , it struc-tured the inner-cluster mapping function for voting , and proposed the inter-cluster voting noise recognition algorithm-NoiseRank .Experimental results show that in environments with intensive Noise-Point distribu-tion, the Noise-data recognition rate of NoiseRank is much higher than that of DBSCAN . |
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| Keywords: | noise-data recognition environments with intensive noise-point distribution inner-cluster voting DBSCAN PageRank |
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