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 51–60 of 113 papers

TitleStatusHype
Times series averaging from a probabilistic interpretation of time-elastic kernel—0
TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering—0
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units—0
Towards Financially Inclusive Credit Products Through Financial Time Series Clustering—0
Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals—0
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks—0
Unsupervised Visual Time-Series Representation Learning and Clustering—0
Using Quantum Mechanics to Cluster Time Series—0
4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients—0
Deep Temporal Contrastive 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