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 81–90 of 113 papers

TitleStatusHype
Learning Representations for Time Series ClusteringCode0
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs—0
DPSOM: Deep Probabilistic Clustering with Self-Organizing MapsCode0
Actor-Critic Approach for Temporal Predictive Clustering—0
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
Linear Dynamics: Clustering without identificationCode0
Clustering piecewise stationary processes—0
A self-organising eigenspace map 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