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
On time series clustering with k-means—0
PARIS: Personalized Activity Recommendation for Improving Sleep Quality—0
Polyspectral Mean based Time Series Clustering of Indian Stock Market—0
Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering—0
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering—0
Reducing statistical time-series problems to binary classification—0
Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering—0
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences—0
Similarity Preserving Representation Learning for Time Series Clustering—0
Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2—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