A Recognition Algorithm of Noise Applied to Environments with Intensive Noise-data Distribution

Chen Pinghua; Zhou Peng

doi:10.3969/j.issn.1007-7162.2014.03.007

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A Recognition Algorithm of Noise Applied to Environments with Intensive Noise-data Distribution

更新时间：2026-05-27

- A Recognition Algorithm of Noise Applied to Environments with Intensive Noise-data Distribution
- Vol. 31, Issue 3, Pages: 39-43(2014)
- 作者机构：
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- DOI：10.3969/j.issn.1007-7162.2014.03.007
  CLC：
- Online First：30 September 2014，
  
  Published：2014
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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]. 广东工业大学学报, 2014, 31(3): 39-43.

Chen Pinghua, Zhou Peng. A Recognition Algorithm of Noise Applied to Environments with Intensive Noise-data Distribution[J]. 2014, 31(3): 39-43.
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]. 广东工业大学学报, 2014, 31(3): 39-43. DOI： 10.3969/j.issn.1007-7162.2014.03.007.

Chen Pinghua, Zhou Peng. A Recognition Algorithm of Noise Applied to Environments with Intensive Noise-data Distribution[J]. 2014, 31(3): 39-43. DOI： 10.3969/j.issn.1007-7162.2014.03.007.

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.

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 structured 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 distribution

the Noise-data recognition rate of NoiseRank is much higher than that of DBSCAN.

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