Clustering validation and identifying the optimal number of clusters are crucial in expert and
intelligent systems. However, the commonly used cluster validity indices (CVI) are not …

Clustering is a set of essential mathematical techniques in artificial intelligence and machine
learning for analyzing massive amounts of data generated by applications. Clustering uses …

Synapses are found in vast numbers in the brain and contain complex proteomes. We
developed genetic labeling and imaging methods to examine synaptic proteins in individual …

Spectral clustering is one of the most popular clustering approaches. However, it is not a
trivial task to apply spectral clustering to large-scale problems due to its computational …

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 …

Background Microbial inhabitants of soils are important to ecosystem and planetary
functions, yet there are large gaps in our knowledge of their diversity and ecology. The …

Under the framework of graph-based learning, the key to robust subspace clustering and
subspace learning is to obtain a good similarity graph that eliminates the effects of errors …

Background Evaluating clinical outcome prior to concurrent chemoradiotherapy remains
challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic …

Motivation The advent of next-generation sequencing technologies enables researchers to
sequence complex microbial communities directly from the environment. Because assembly …

Role of technology is improving for COVID-19 management all around the world. Usage of
mobile applications, web applications, cloud computing, and related technologies have …