摘要:聚类作为无监督学习技术，已在实际中得到了广泛的应用.但是对于带有噪声的数据集，一些主流算法仍然存在着噪声去除不彻底和聚类结果不准确等问题.提出了一种基于密度差分的自动聚类算法（clustering based on density difference，简称CDD），实现了对含有噪声数据集的自动分类.所提算法根据噪声数据和有用数据密度的不同，实现了去噪声和数据的分类，并通过构建数据间的邻域，进一步实现了对有用数据间不同类别的划分.通过实验验证了所提算法的有效性.



Abstract:As an unsupervised learning technology, clustering has been widely used in practice. However, some mainstream algorithms still have problems such as incomplete noise removal and inaccurate clustering results for the datasets with noise. In this paper, an automatic clustering algorithm based on density difference (CDD) is proposed to realize automatic classification of the datasets containing the noise. The algorithm is based on the density difference between noise data and useful data to achieve removing noise and data classification. Moreover, the useful data are classified into different classes through the neighborhood construction procedure. Experimental results demonstrate that the CDD algorithm has high performance.



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