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Unsupervised gene selection using biological knowledge : application in sample clustering [PDF]
BMC Bioinformatics, 2017 Background Classification of biological samples of gene expression data is a basic building block in solving several problems in the field of bioinformatics like cancer and other disease diagnosis and making a proper treatment plan.
Sudipta Acharya +2 more
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Research of nonparametric density estimation algorithms by applying clustering methods
Lietuvos Matematikos Rinkinys, 2023 One of the ways to improve the accuracy of probability density estimation is multi-mode density treating as the mixture of single-mode one. In this paper we offer to use data clustering in the first place and to estimate density in every cluster ...
Rasa Šmidtaitė, Tomas Ruzgas
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Density-Peak Clustering Algorithm on Decentralized and Weighted Clusters Merging [PDF]
Jisuanji kexue yu tansuo, 2022 The clustering by fast search and find of density peaks (DPC) is a density-based clustering algorithm proposed in recent years, which has the advantages of simple principle, no iteration and clustering of arbitrary shape. However, the algorithm still has
ZHAO Liheng, WANG Jian, CHEN Hongjun
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Deep Subspace Clustering Algorithm with Data Augmentation and Adaptive Self-Paced Learning [PDF]
Jisuanji gongcheng, 2023 Deep subspace clustering achieves better performance than traditional clustering by jointly performing self-expressed feature learning and cluster allocation.Despite the emergence of a large number of deep subspace clustering algorithms in various ...
Yuyan JIANG, Chengfeng TAO, Ping LI
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Refined Mode-Clustering via the Gradient of Slope
Stats, 2021 In this paper, we propose a new clustering method inspired by mode-clustering that not only finds clusters, but also assigns each cluster with an attribute label. Clusters obtained from our method show connectivity of the underlying distribution. We also
Kunhui Zhang, Yen-Chi Chen
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Density Gain-Rate Peaks for Spectral Clustering
IEEE Access, 2021 Clustering has been troubled by varying shapes of sample distributions, such as line and spiral shapes. Spectral clustering and density peak clustering are two feasible techniques to address this problem, and have attracted much attention from academic ...
Jiexing Liu, Chenggui Zhao
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Density Peak Clustering Algorithm Based on Relative Density [PDF]
Jisuanji gongcheng, 2023 When the density peak clustering algorithm deals with datasets with uneven density，it is easy to divide the low-density clusters into high-density clusters，divide the high-density clusters into multiple sub-clusters，and exists the error propagation ...
WEI Ya, ZHANG Zhengjun, HE Kailin, TANG Li
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Robust Spectral Clustering via Low-Rank Sample Representation
Applied Computational Intelligence and Soft Computing, 2022 Traditional clustering methods neglect the data quality and perform clustering directly on the original data. Therefore, their performance can easily deteriorate since real-world data would usually contain noisy data samples in high-dimensional space. In
Hao Liang +3 more
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Mathematical Modelling and Analysis, 2020 Flexible and reliable probability density estimation is fundamental in unsupervised learning and classification. Finite Gaussian mixture models are commonly used for this purpose. However, the parametric form of the distribution is not always known.
Jurgita Arnastauskaitė, Tomas Ruzgas
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IEEE Access, 2021 The clustering effect of the spectral clustering algorithm depends on the calculation of the similarity between samples. Although a better clustering effect of the spectral clustering algorithm can be obtained using the Gaussian kernel function to ...
Gen Zhang +4 more
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