Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and
statistical inference. There has been a recent upsurge in the application of latent class …

In the present day, there are many diseases which need to be identified at their early stages
to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason …

Clustering has primarily been used as an analytical technique to group unlabeled data for
extracting meaningful information. The fact that no clustering algorithm can solve all …

Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …

A central goal of systems biology is to elucidate the structural and functional architecture of
the cell. To this end, large and complex networks of molecular interactions are being rapidly …

This work introduces the need to develop competitive, low-cost and applicable technologies
to real roads to detect the asphalt condition by means of Machine Learning (ML) algorithms …

Data clustering has been proven to be an effective method for discovering structure in
medical datasets. The majority of clustering algorithms produce exclusive clusters meaning …

Identifying groups of similar objects is a popular first step in biomedical data analysis, but it
is error-prone and impossible to perform manually. Many computational methods have been …

Metabolomics is a relatively new high-throughput technology that aims at measuring all
endogenous metabolites within a biological sample in an unbiased fashion. The resulting …

Applications of clustering algorithms in biomedical research are ubiquitous, with typical
examples including gene expression data analysis, genomic sequence analysis, biomedical …