Deep learning-based clustering approaches for bioinformatics
Clustering is central to many data-driven bioinformatics research and serves a powerful
computational method. In particular, clustering helps at analyzing unstructured and high …
computational method. In particular, clustering helps at analyzing unstructured and high …
A survey on time-series pre-trained models
Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …
practical applications. Deep learning models that rely on massive labeled data have been …
Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system
The performance of decomposition-based algorithms is sensitive to the Pareto front shapes
since their reference vectors preset in advance are not always adaptable to various problem …
since their reference vectors preset in advance are not always adaptable to various problem …
Clustering algorithms: A comparative approach
Many real-world systems can be studied in terms of pattern recognition tasks, so that proper
use (and understanding) of machine learning methods in practical applications becomes …
use (and understanding) of machine learning methods in practical applications becomes …
Learning representations for time series clustering
Time series clustering is an essential unsupervised technique in cases when category
information is not available. It has been widely applied to genome data, anomaly detection …
information is not available. It has been widely applied to genome data, anomaly detection …
FairVis: Visual analytics for discovering intersectional bias in machine learning
The growing capability and accessibility of machine learning has led to its application to
many real-world domains and data about people. Despite the benefits algorithmic systems …
many real-world domains and data about people. Despite the benefits algorithmic systems …
A comparison study on similarity and dissimilarity measures in clustering continuous data
Similarity or distance measures are core components used by distance-based clustering
algorithms to cluster similar data points into the same clusters, while dissimilar or distant …
algorithms to cluster similar data points into the same clusters, while dissimilar or distant …
A benchmark study on time series clustering
This paper presents the first time series clustering benchmark utilizing all time series
datasets currently available in the University of California Riverside (UCR) archive—the …
datasets currently available in the University of California Riverside (UCR) archive—the …
Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application
Alzheimer's disease (AD) is the most common form of dementia, characterized by
progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic …
progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic …
IoT network slicing on virtual layers of homogeneous data for improved algorithm operation in smart buildings
With its strong coverage, low energy consumption, low cost and great connectivity, the
Internet of Things technology has become the key technology in smart cities. However, faced …
Internet of Things technology has become the key technology in smart cities. However, faced …