Time series \(k\)-means: a new \(k\)-means type smooth subspace clustering for time series data

DOI10.1016/j.ins.2016.05.040zbMath1428.62276OpenAlexW2408821405MaRDI QIDQ2282012

Yanyan Li

Publication date: 6 January 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2016.05.040

zbMATH Keywords

time seriesdata miningfeature selection\(k\)-means clusteringsubspace clustering

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Mining interpretable spatio-temporal logic properties for spatially distributed systems, Incremental fuzzy clustering of time series, A testing approach to clustering scalar time series, A new distance with derivative information for functional \(k\)-means clustering algorithm, On clustering uncertain and structured data with Wasserstein barycenters and a geodesic criterion for the number of clusters

Uses Software

• UCR Suite

Cites Work

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