The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …

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 …

Clustering techniques are widely used in the analysis of large datasets to group together
samples with similar properties. For example, clustering is often used in the field of single …

Clustering non-Euclidean data is difficult, and one of the most used algorithms besides
hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also …

Clustering is central to many data-driven bioinformatics research and serves a powerful
computational method. In particular, clustering helps at analyzing unstructured and high …

Determining how cells vary with their local signaling environment and organize into distinct
cellular communities is critical for understanding processes as diverse as development …

Clustering non-Euclidean data is difficult, and one of the most used algorithms besides
hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also …

With the advent of Big Data, nowadays in many applications databases containing large
quantities of similar time series are available. Forecasting time series in these domains with …

A major challenge when using k-means clustering often is how to choose the parameter k,
the number of clusters. In this letter, we want to point out that it is very easy to draw poor …

Correlation and association analyses are one of the most widely used statistical methods in
research fields, including microbiome and integrative multiomics studies. Correlation and …