Machine learningClusteringHigh predictive abilityGenetic algorithmMaterial designMolecular designOPTIMIZATIONIn the design of molecules, materials, and processes, a mathematical model y = f(x) is constructed to establish a relationship between the explanatory variable x and the objective variable y using a...
6.7.4.2Text clustering According to its different characteristics, the data can be divided into different data clusters. The purpose is to make the distance between individuals belonging to the same category as small as possible, while the distance between individuals in different categories is as la...
A clustering ensemble aims to combine multiple clustering models to produce a better result than that of the individual clustering algorithms in terms of c
To show the process of hierarchical clustering, we generated a dataset X consisting of 10 data points with 2 dimensions. Then, the “ward” method is used from theSciPylibrary to perform hierarchical clustering on the dataset by calling the linkage function. After that, the dendrogram function i...
Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive ...
Clustering techniques are widely used in many applications. The goal of clustering is to identify patterns or groups of similar objects within a dataset of interest. However, many cluster methods are neither robust nor sensitive to noises and outliers in real data. In this paper, we present Nucl...
Structured association techniques are unsupervised learning (clustering) methods such as STRUCTURE[30] which is based on a Bayesian framework and latent class analysis[31] which is based on maximum-likelihood that assign subjects of a case-control study cohort to discrete subpopulations based on their...
explained by the known petrological processes. The application of our trained machine learning classifiers to detrital zircon studies will enhance the interpretability of zircon assemblages of different origins. It also helps develop interpretations, approaches, and tools that will benefit, for example, ...
3.4 Clustering-based methods The clustering technique is a kind of machine learning algorithm to classify data. In the scenario reduction analysis, “representative scenarios” are desired to get by clustering. The commonly-used clustering algorithms include partitioning clustering and hierarchical clustering...
The methods of machine learning and deep learning are similar to the segmentation method based on clustering ideas. The goal is to find a suitable metric in a high-dimensional space so that in the new space, pixels with the same characteristics are mutually close, and the distance between ...