SOTAVerified

Time Series Clustering

Time Series Clustering is an unsupervised data mining technique for organizing data points into groups based on their similarity. The objective is to maximize data similarity within clusters and minimize it across clusters. Time-series clustering is often used as a subroutine of other more complex algorithms and is employed as a standard tool in data science for anomaly detection, character recognition, pattern discovery, visualization of time series.

Source: Comprehensive Process Drift Detection with Visual Analytics

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 81–90 of 113 papers

TitleStatusHype
Kernel Spectral Clustering and applications—0
Latent Space Semi-Supervised Time Series Data Clustering—0
Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale—0
CRAD: Clustering with Robust Autocuts and DepthCode0
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series ClusteringCode0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Deep Markov Spatio-Temporal FactorizationCode0
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency RepresentationCode0
k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image SequencesCode0
Time Series Clustering via Community Detection in NetworksCode0
Show:102550
← PrevPage 9 of 12Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SOM-VAE-probNMI (physiology_6_hours)0.05—Unverified
2k-meansNMI (physiology_6_hours)0.04—Unverified
3SOM-VAENMI (physiology_6_hours)0.04—Unverified