Density decay graph-based density peak clustering Knowledge-Based Systems, Volume 224, 2021, Article 107075 Zhiyong Zhang,…, Jiangmei Luo Density peak clustering using global and local consistency adjustable manifold distance Information Sciences, Volume 577, 2021, pp. 769-804 Xinmin Tao,…, Junrong...
Density peak clustering (DPC) is a recently developed density-based clustering algorithm that achieves competitive performance in a non-iterative manner. DPC is capable of effectively handling clusters with single density peak (single center), i.e., based on DPC's hypothesis, one and only ...
The density peak clustering algorithm treats local density peaks as cluster centers, and groups non-center data points by assuming that one data point and its nearest higher-density neighbor are in the same cluster. While this algorithm is shown to be promising in some applications, its clustering...
To solve the problems, a new clustering method, density peak clustering based on cumulative nearest neighbors degree and micro cluster merging, is proposed. The proposed method improves the DPC algorithm in two ways, the one is that the method defines a new local density to solve the defect ...
To judge the number of shadows in a soil image used for soil species identification, density peak clustering for shadow detection of soil image (DPCSDSI) is proposed to segment shadows. Firstly, the [Math Processing Error]Bright histogram of a soil image is built and its Gaussian smoothing is...
虽然有DBSCAN(density-based spatial clustering of applications with noise)对于任意形状分布的进行聚类,但是必须指定一个密度阈值,从而去除低于此密度阈值的噪音点。 这篇文章假设聚类中心周围都是密度比其低的点,同时这些点距离该聚类中心的距离相比于其他聚类中心最近。
Kmean算法有很多不足的地方,比如k值的确定,初始结点选择,而且还不能检测费球面类别的数据分布,对于第二个问题,提出了Kmean++,而其他不足还没有解决,dbscan虽然可以对任意形状分布的进行聚类,但是必须指定一个密度阈值,从而去除低于此密度阈值的噪音点,这篇文章解决了这些不足。
一种新型聚类算法(Clustering by fast search and find of density peaksd) 转自http://blog.csdn.net/lvxiong1990/article/details/40540065 参考文献 Rodriguez, Alex, and Alessandro Laio. "Clustering by fast search and find of density peaks." Science 344.6191 (2014): 1492-1496. ...
In this paper, we focused on the popular Density Peak Clustering (DPC) algorithm, which is used in many applications. However, its inability to deal with varying density clusters and sensitivity to the representation of data limit its effectiveness for real-world problems where: (i) data have ...
class DensityPeakCluster(object): """ Density Peak Cluster. Methods: fit: fit model plot: plot clustering Attributes: n_id: data row count distance: each id distance dc: threshold of density cut off rho: each id density nneigh: each id min upper density nearest neighbor delta: each id ...