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DBSCAN聚类

于 2018-02-05 发布 文件大小:10KB
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  Python密度聚类 最近在Science上的一篇基于密度的聚类算法《Clustering by fast search and find of density peaks》引起了大家的关注(在我的博文“论文中的机器学习算法——基于密度峰值的聚类算法”中也进行了中文的描述)。于是我就想了解下基于密度的聚类算法,熟悉下基于密度的聚类算法与基于距离的聚类算法,如K-Means算法之间的区别。 基于密度的聚类算法主要的目标是寻找被低密度区域分离的高密度区域。与基于距离的聚类算法不同的是,基于距离的聚类算法的聚类结果是球状的簇,而基于密度的聚类算法可以发现任意形状的聚类,这对于带有噪音点的数据起着重要的作用。(The main goal of the density based clustering algorithm is to find high density regions separated by low density regions. Different from distance based clustering algorithm, the clustering results based on distance clustering algorithm are spherical clusters, and density based clustering algorithm can detect clustering of arbitrary shapes, which plays an important role in data with noisy points.)

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DBSCAN聚类.docx, 12450 , 2018-01-31

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