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: 6101
Venue: SIGMOD
Year: 2021
Pagerank: 5.4756587e-05
Overall Rank: 8,159 | 44.03%
DOI: 10.1145/3448016.3452757

Incoming Non-self Citations Over Time

Authors

1. Donato Tiano (Lyon 1 University)
2. Angela Bonifati (Lyon 1 University)
3. Raymond Ng (University of British Columbia)

BibTeX Citation

@inproceedings{tiano_sigmod21,
        title = {{FeatTS: Feature-based Time Series Clustering}},
        author = {Tiano, Donato and Bonifati, Angela and Ng, Raymond},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452757},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452757},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,038	Time2Feat: Learning Interpretable Representations for Multivariate Time Series Clustering	2023	VLDB	6.3911449e-05
10,978	Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods	2025	VLDB	5.093636e-05
11,266	DARKER: Efficient Transformer with Data-driven Attention Mechanism for Time Series	2024	VLDB	5.093636e-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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