Clustering is a well-known unsupervised machine learning approach capable of automatically grouping discrete sets of instances with similar characteristics
In this paper, we investigate this aspect and propose a (hierarchical) multi-label classification method based on semi-supervised learning of predictive clustering trees, which we also extend towards ensemble learning. Extensive experimental evaluation conducted on 24 datasets shows sign...
Choosing an unsupervised ML algorithm for clustering For clustering purposes, we utilize HDBSCAN (see Box 2), since it suggests a number of clusters. It can be seen that some clusters are probably not correctly classified, which might be mended by tinkering with the UMAP hyperparameters to get...
One of the major tasks with the gene expression data is to find groups of co regulated genes whose collective expression is strongly associated with the sample categories or response variables. In this regard, a new supervised attribute clustering algorithm is proposed to find such groups of genes...
A comparative study of five unsupervised multiomics integration methods demonstrated that combining more omics can improve the accuracy of clustering but on the other hand might add noise and decrease signal strength[25]. This highlights the opportunity and the danger of adding more and more layers...
PiCIE [46] obtains semantically meaningful segmentation without labels by jointly learning clustering and representation consistency under photometric and geometric perturbations. MixMatch [49] encourages consistency between predictions in different MixUp perturbations of the same input. The average prediction is...
While ready-to-use algorithms from research institutions are available, local adaptation is crucial to capture the specific conditions of the region (Liu et al. 2021; Vaz et al. 2023). Most studies rely on unsupervised multivariate techniques, such as clustering and principal component analysis (...
Learning interpretable and disentangled representations has been considered in the β−VAE [9] which uses a large penalty on the Kullback–Leibler (KL) divergence term of the loss function, in order to encourage the independence between latent variables. However, a large penalty would sacrifice th...
聚类(Clustering) Group similar data points together. 异常检测(Anomaly Detection) Find unusual data points. 降维(Dimensionality reduction) Compress data using fewer numbers. 4. 线性回归(Linear Regression) 线性回归是回归的一个例子,它搭建一个线性模型,从而预测回归问题。
some clusters must be subdivided or combined to make this correspondence; the analyst might also choose to modify the class labels based on the clustering results. The labeled cluster data can then be used in a finalsupervised classification, or the labeled cluster map can be simply accepted as...