Biological sequence classification: A review on data and general methods
C Ao, S Jiao, Y Wang, L Yu, Q Zou - Research, 2022 - spj.science.org
With the rapid development of biotechnology, the number of biological sequences has
grown exponentially. The continuous expansion of biological sequence data promotes the …
grown exponentially. The continuous expansion of biological sequence data promotes the …
Time-series clustering–a decade review
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 …
about classes. With emerging new concepts like cloud computing and big data and their vast …
Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets
The all-pairs-similarity-search (or similarity join) problem has been extensively studied for
text and a handful of other datatypes. However, surprisingly little progress has been made …
text and a handful of other datatypes. However, surprisingly little progress has been made …
Multi-scale convolutional neural networks for time series classification
Time series classification (TSC), the problem of predicting class labels of time series, has
been around for decades within the community of data mining and machine learning, and …
been around for decades within the community of data mining and machine learning, and …
k-shape: Efficient and accurate clustering of time series
The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …
substantial interest in the analysis and mining of time series. Clustering is one of the most …
Minimum redundancy maximum relevance feature selection approach for temporal gene expression data
Background Feature selection, aiming to identify a subset of features among a possibly large
set of features that are relevant for predicting a response, is an important preprocessing step …
set of features that are relevant for predicting a response, is an important preprocessing step …
Time series classification using multi-channels deep convolutional neural networks
Time series (particularly multivariate) classification has drawn a lot of attention in the
literature because of its broad applications for different domains, such as health informatics …
literature because of its broad applications for different domains, such as health informatics …
The BOSS is concerned with time series classification in the presence of noise
P Schäfer - Data Mining and Knowledge Discovery, 2015 - Springer
Similarity search is one of the most important and probably best studied methods for data
mining. In the context of time series analysis it reaches its limits when it comes to mining raw …
mining. In the context of time series analysis it reaches its limits when it comes to mining raw …
Toeplitz inverse covariance-based clustering of multivariate time series data
Subsequence clustering of multivariate time series is a useful tool for discovering repeated
patterns in temporal data. Once these patterns have been discovered, seemingly …
patterns in temporal data. Once these patterns have been discovered, seemingly …
TSclust: An R package for time series clustering
P Montero, JA Vilar - Journal of statistical software, 2015 - jstatsoft.org
Time series clustering is an active research area with applications in a wide range of fields.
One key component in cluster analysis is determining a proper dissimilarity measure …
One key component in cluster analysis is determining a proper dissimilarity measure …