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

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Showing 91–100 of 113 papers

TitleStatusHype
Bridging the Gap: A Decade Review of Time-Series Clustering Methods—0
Clustering evolving data using kernel-based methods—0
Clustering Macroeconomic Time Series—0
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology—0
Clustering of Urban Traffic Patterns by K-Means and Dynamic Time Warping: Case Study—0
Clustering piecewise stationary processes—0
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization—0
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework—0
Concrete Dense Network for Long-Sequence Time Series Clustering—0
Coresets for Time Series Clustering—0
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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