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 101–113 of 113 papers

TitleStatusHype
Deep Temporal Contrastive Clustering—0
Detecting CAN Masquerade Attacks with Signal Clustering Similarity—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
Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data—0
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering—0
Dynamic clustering of time series data—0
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs—0
Evaluation of k-means time series clustering based on z-normalization and NP-Free—0
Examining the Dynamics of Local and Transfer Passenger Share Patterns in Air Transportation—0
Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data—0
Fuzzy clustering of ordinal time series based on two novel distances with economic applications—0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—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