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1、Clustering by fast search and find of density peaks,姓名:潘培哲 专业:控制工程 学号:12013002347,基于密度峰值的聚类算法,文章来源,摘自2014-6-27Science期刊,Clustering by fast search and find of density peaks算法思想,Clustering by fast search and find of density peaks算法介绍,Clustering by fast search and find of density peaks算法介绍,Clustering b
2、y fast search and find of density peaks算法介绍,Clustering by fast search and find of density peaks算法介绍,1,2,3,Clustering by fast search and find of density peaks聚类中心选取,Clustering by fast search and find of density peaks聚类中心选取,Clustering by fast search and find of density peaks聚类过程,类簇中心找到后,剩余的每个点被归属到它的有更
3、高密度的最近邻所属类簇。类簇分配只需一步即可完成,不像其它算法要对目标函数进行迭代优化。,Clustering by fast search and find of density peaks算法存在的问题,下图中的图B(决策图)对确定聚类中心具有决定性作用,但是,对聚类中心的选取需要人为干预,而且其中包含了主观因素,不同的人可能选择不同的点作为聚类中心。,决策图中聚类中心难以确定的例子,Clustering by fast search and find of density peaks算法优化,Clustering by fast search and find of density pe
4、aks算法存在的问题,按照原文的聚类方法,一些分散的“离群点”也会被“强制”分到类簇当中,造成聚类后类簇边界不清晰,影响聚类效果。,Clustering by fast search and find of density peaks算法优化,Clustering by fast search and find of density peaks聚类结果展示,不区分cluster halo的聚类结果,Clustering by fast search and find of density peaks聚类结果展示,区分cluster halo的聚类结果,Clustering by fast search and find of density peaks其他聚类结果展示,对不同形状的类簇聚类效果都很好,结束!,姓名:潘培哲 专业:控制工程 学号:12013002347,谢谢观赏,