From clustering to clustering ensemble selection: A review
Clustering, as an unsupervised learning, is aimed at discovering the natural grou**s of a
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …
An overview of end-to-end entity resolution for big data
One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …
AutoML: state of the art with a focus on anomaly detection, challenges, and research directions
The last decade has witnessed the explosion of machine learning research studies with the
inception of several algorithms proposed and successfully adopted in different application …
inception of several algorithms proposed and successfully adopted in different application …
Clustering by fast search and find of density peaks
Cluster analysis is aimed at classifying elements into categories on the basis of their
similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern …
similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern …
Shared-nearest-neighbor-based clustering by fast search and find of density peaks
Clustering by fast search and find of density peaks (DPC) is a new clustering method that
was reported in Science in June 2014. This clustering algorithm is based on the assumption …
was reported in Science in June 2014. This clustering algorithm is based on the assumption …
dbscan: Fast density-based clustering with R
This article describes the implementation and use of the R package dbscan, which provides
complete and fast implementations of the popular density-based clustering algorithm …
complete and fast implementations of the popular density-based clustering algorithm …
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 …
Identification of cell types from single-cell transcriptomes using a novel clustering method
Motivation: The recent advance of single-cell technologies has brought new insights into
complex biological phenomena. In particular, genome-wide single-cell measurements such …
complex biological phenomena. In particular, genome-wide single-cell measurements such …