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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 21–30 of 113 papers

TitleStatusHype
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering—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
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Coresets for Time Series Clustering—0
Autoencoder-based time series clustering with energy applications—0
Actor-Critic Approach for Temporal Predictive 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