Recent advances in sequencing, mass spectrometry and cytometry technologies have
enabled researchers to collect large-scale omics data from the same set of biological …

Data clustering involves identifying latent similarities within a dataset and organizing them
into clusters or groups. The outcomes of various clustering algorithms differ as they are …

Comparing two clustering results of a data set is a challenging task in cluster analysis. Many
external validity measures have been proposed in the literature. A good measure should be …

In unsupervised machine learning, agreement between partitions is commonly assessed
with so-called external validity indices. Researchers tend to use and report indices that …

Accurately identifying cell-populations is paramount to the quality of downstream analyses
and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a …

Clustering is one of the most universal approaches for understanding complex data. A
pivotal aspect of clustering analysis is quantitatively comparing clusterings; clustering …

Most microbes cannot be easily cultured, and metagenomics provides a means to study
them. Current techniques aim to resolve individual genomes from metagenomes, so-called …

Time-series clustering of view counts with changes in online time can identify animated
series with similar evolutionary count patterns over time, which may help companies reduce …

The true cluster number of the dataset in practical applications is rarely known in advance.
Therefore, it is necessary to use a cluster validity index to evaluate the clustering results and …

Many external validity indices for comparing different clusterings of the same set of objects
are overall measures: they quantify similarity between clusterings for all clusters …