Machine learning methods for small data challenges in molecular science
B Dou, Z Zhu, E Merkurjev, L Ke, L Chen… - Chemical …, 2023 - ACS Publications
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions
Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …
groups so that similar samples belong to the same cluster while dissimilar samples belong …
Slide-tags enables single-nucleus barcoding for multimodal spatial genomics
Recent technological innovations have enabled the high-throughput quantification of gene
expression and epigenetic regulation within individual cells, transforming our understanding …
expression and epigenetic regulation within individual cells, transforming our understanding …
A comprehensive survey of clustering algorithms
Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …
communication science, computer science and biology science. Clustering, as the basic …
A survey of density based clustering algorithms
P Bhattacharjee, P Mitra - Frontiers of Computer Science, 2021 - Springer
Density based clustering algorithms (DBCLAs) rely on the notion of density to identify
clusters of arbitrary shapes, sizes with varying densities. Existing surveys on DBCLAs cover …
clusters of arbitrary shapes, sizes with varying densities. Existing surveys on DBCLAs cover …
A survey of clustering algorithms for big data: Taxonomy and empirical analysis
Clustering algorithms have emerged as an alternative powerful meta-learning tool to
accurately analyze the massive volume of data generated by modern applications. In …
accurately analyze the massive volume of data generated by modern applications. In …
Algorithms for hierarchical clustering: an overview, II
F Murtagh, P Contreras - Wiley Interdisciplinary Reviews: Data …, 2017 - Wiley Online Library
We survey agglomerative hierarchical clustering algorithms and discuss efficient
implementations that are available in R and other software environments. We look at …
implementations that are available in R and other software environments. We look at …
Study on density peaks clustering based on k-nearest neighbors and principal component analysis
Density peaks clustering (DPC) algorithm published in the US journal Science in 2014 is a
novel clustering algorithm based on density. It needs neither iterative process nor more …
novel clustering algorithm based on density. It needs neither iterative process nor more …
Hybrid fruit-fly optimization algorithm with k-means for text document clustering
The fast-growing Internet results in massive amounts of text data. Due to the large volume of
the unstructured format of text data, extracting relevant information and its analysis becomes …
the unstructured format of text data, extracting relevant information and its analysis becomes …
Algorithms for hierarchical clustering: an overview
F Murtagh, P Contreras - Wiley Interdisciplinary Reviews: Data …, 2012 - Wiley Online Library
We survey agglomerative hierarchical clustering algorithms and discuss efficient
implementations that are available in R and other software environments. We look at …
implementations that are available in R and other software environments. We look at …