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 101–113 of 113 papers

TitleStatusHype
Similarity Preserving Representation Learning for Time Series Clustering—0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy—0
Time Series Structure Discovery via Probabilistic Program Synthesis—0
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering—0
Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2—0
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency RepresentationCode0
Time Series Clustering via Community Detection in NetworksCode0
Times series averaging from a probabilistic interpretation of time-elastic kernel—0
Kernel Spectral Clustering and applications—0
Clustering evolving data using kernel-based methods—0
An Empirical Evaluation of Similarity Measures for Time Series Classification—0
Model-based clustering with Hidden Markov Model regression for time series with regime changes—0
Reducing statistical time-series problems to binary classification—0
Show:102550
← PrevPage 3 of 3Next →

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