YADING: Fast Clustering of Large-Scale Time Series Data

Summary: YADING scales time-series clustering via theoretically bounded sampling, clustering, and assignment, preserving dataset distributions. L1 similarity and multi-density clustering provide robustness to phase shifts/noise, yielding up to 1,000× speedups over DBSCAN/CLARANS. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 11347
Venue: VLDB
Year: 2015
Pagerank: 6.8703273e-05
Overall Rank: 4,151 | 71.53%
DOI: 10.14778/2735479.2735481

Incoming Non-self Citations Over Time

Authors

1. Rui Ding (Microsoft)
2. Qiang Wang (Microsoft)
3. Yingnong Dang (Microsoft)
4. Qiang Fu (Microsoft)
5. Haidong Zhang (Microsoft)
6. Dongmei Zhang (Microsoft)

BibTeX Citation

@article{ding_vldb15,
        title = {{YADING: Fast Clustering of Large-Scale Time Series Data}},
        author = {Ding, Rui and Wang, Qiang and Dang, Yingnong and Fu, Qiang and Zhang, Haidong and Zhang, Dongmei},
        journal = {PVLDB},
        series = {{VLDB} '15},
        volume = {8},
        number = {5},
        pages = {473--484},
        doi = {10.14778/2735479.2735481},
        url = {https://doi.org/10.14778/2735479.2735481},
        year = {2015}
}

Incoming Citations (Sorted by Pagerank)

Showing 11 of 11 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,234	GRAIL: Efficient Time-Series Representation Learning	2019	VLDB	6.8171563e-05
4,604	Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures	2020	SIGMOD	6.6104585e-05
10,629	MS-Index: Fast Top-k Subsequence Search for Multivariate Time Series under Euclidean Distance	2026	VLDB	5.093636e-05
10,747	A Structured Study of Multivariate Time-Series Distance Measures	2025	SIGMOD	5.093636e-05
10,923	Improving Time Series Data Compression in Apache IoTDB	2025	VLDB	5.093636e-05
10,959	Federated and Balanced Clustering for High-dimensional Data	2025	VLDB	5.093636e-05
10,978	Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods	2025	VLDB	5.093636e-05
11,107	Representative Time Series Discovery for Data Exploration	2025	VLDB	5.093636e-05
11,348	Cache-Efficient Top-k Aggregation over High Cardinality Large Datasets	2024	VLDB	5.093636e-05
11,435	Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances	2023	VLDB	5.093636e-05
11,538	Scalable Time Series Compound Infrastructure	2022	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
4	The R*-tree: An Efficient and Robust Access Method for Points and Rectangles	1990	SIGMOD	0.001157935
85	The X-tree: An Index Structure for High-Dimensional Data	1996	VLDB	0.00035405879
88	Efficient and Effective Clustering Methods for Spatial Data Mining	1994	VLDB	0.00035240327
190	Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases	2001	SIGMOD	0.00026105472
291	OPTICS: Ordering Points To Identify the Clustering Structure	1999	SIGMOD	0.00022264197
351	CURE: An Efficient Clustering Algorithm for Large Databases	1998	SIGMOD	0.00020424271
468	Fast Time Sequence Indexing for Arbitrary Lp Norms	2000	VLDB	0.00017986163
1,285	STING: A Statistical Information Grid Approach to Spatial Data Mining	1997	VLDB	0.00011321748
3,252	Fast Time-Series Searching with Scaling and Shifting	1999	PODS	7.5951443e-05

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