Weighted K-nearest Neighbors and Multi-cluster Merge Density Peaks Clustering Algorithm [PDF]
Jisuanji kexue yu tansuo, 2022 Density peaks clustering (DPC) algorithm is a clustering algorithm based on density. The algorithm is simple in principle and efficient in operation, and can find any non-spherical class clusters. However, there are some defects in the algorithm. Firstly,
CHEN Lei, WU Runxiu, LI Peiwu, ZHAO Jia
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Local-Sample-Weighted Clustering Ensemble with High-Order Graph Diffusion
Mathematics, 2023 The clustering ensemble method has attracted much attention because it can improve the stability and robustness of single clustering methods. Among them, similarity-matrix-based methods or graph-based methods have had a wide range of applications in ...
Jianwen Gan, Yunhui Liang, Liang Du
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Application of Clustering in the Non-Parametric Estimation of Distribution Density
Nonlinear Analysis, 2006 This paper discusses a multimodal density function estimation problem of a random vector. A comparative accuracy analysis of some popular non-parametric estimators is made by using the Monte-Carlo method.
T. Ruzgas, R. Rudzkis, M. Kavaliauskas
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Clustering on very small scales from a large sample of confirmed quasar pairs: Does quasar clustering track from Mpc to kpc scales? [PDF]
, 2017 We present the most precise estimate to date of the clustering of quasars on very small scales, based on a sample of 47 binary quasars with magnitudes of ...
Djorgovski, S. G. +6 more
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An Improved K-Means Algorithm Based on Evidence Distance
Entropy, 2021 The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points.
Ailin Zhu +4 more
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Frontiers in Genetics, 2019 Non-negative matrix factorization (NMF) is a matrix decomposition method based on the square loss function. To exploit cancer information, cancer gene expression data often uses the NMF method to reduce dimensionality.
Chuan-Yuan Wang +3 more
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A novel image clustering method based on coupled convolutional and graph convolutional network
EAI Endorsed Transactions on Scalable Information Systems, 2021 Image clustering is a key and challenging task in the field of machine learning and computer vision. Technically, image clustering is the process of grouping images without the use of any supervisory information in order to retain similar images within ...
Rangjun Li
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The angular correlation function of the ROSAT All Sky Survey Bright Source Catalogue [PDF]
, 1999 We have derived the angular correlation function of a sample of 2096 sources detected in the ROSAT All Sky Survey Bright Source Catalogue, in order to investigate the clustering properties of AGN in the local Universe.
Akylas, A. +2 more
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Sample labeler for crowd counting based on a first neighbor clustering
Xi'an Gongcheng Daxue xuebao, 2021 In terms of crowd counting methods, usually a large number of labeled samples were meeded to train a counting model. In practical applications, to solve the problem of high cost and low efficiency of manually labeling samples, a new sample labeling ...
Kaibing ZHANG +3 more
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Quasar clustering: evidence for an increase with redshift and implications for the nature of AGNs [PDF]
, 1997 The evolution of quasar clustering is investigated with a new sample of 388 quasars with 0 ...
Bellenger R. +17 more
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