FeatTS: Feature-based Time Series Clustering

Summary: FeatTS: feature-based semi-supervised clustering for heterogeneous time series. Graph-encoded features, community detection, and a Co-Occurrence matrix fuse results; visualization enables feature/label tuning and supports domain-specific data (healthcare). (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6039
Venue: SIGMOD
Year: 2021
Pagerank: 4.5650405e-05
Overall Rank: 8,187 | 43.05%
DOI: 10.1145/3448016.3452757

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,851	Time2Feat: Learning Interpretable Representations for Multivariate Time Series Clustering	2023	VLDB	4.9084229e-05
10,739	Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods	2025	VLDB	4.1945683e-05
11,059	DARKER: Efficient Transformer with Data-driven Attention Mechanism for Time Series	2024	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank

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7,278	Interpretable Clustering of Multivariate Time Series with Time2Feat	2023	VLDB	4.7793885e-05
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