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 31–40 of 113 papers

TitleStatusHype
Detecting CAN Masquerade Attacks with Signal Clustering Similarity—0
Deep Temporal Contrastive Clustering—0
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering—0
A Benchmark Study on Time Series Clustering—0
Determining the Optimal Number of Clusters for Time Series Datasets with Symbolic Pattern Forest—0
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping—0
Clustering evolving data using kernel-based methods—0
Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data—0
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering—0
An Empirical Evaluation of Similarity Measures for Time Series Classification—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