This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …

Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

The great success of machine learning with massive amounts of data comes at a price of
huge computation costs and storage for training and tuning. Recent studies on dataset …

Differential abundance (DA) analysis of microbiome data continues to be a challenging
problem due to the complexity of the data. In this article we define the notion of “sampling …

Microbiome differential abundance analysis methods for two groups are well-established in
the literature. However, many microbiome studies involve more than two groups, sometimes …

This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …

Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

The popularity of deep learning has led to the curation of a vast number of massive and
multifarious datasets. Despite having close-to-human performance on individual tasks …

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 …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …