一种应用于噪声点分布密集环境下的噪声点识别算法

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。%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 .