k-Shape: Efficient and Accurate Clustering of Time Series
Summary: k-Shape introduces a scalable iterative refinement for time-series clustering using a normalized cross-correlation distance to emphasize shape. Robust centroid computation yields homogeneous, well-separated clusters, with experiments showing it outperforms scalable baselines and most non-scalable rivals in accuracy. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. John Paparrizos (Columbia University)
- 2. Luis Gravano (Columbia University)
BibTeX Citation
@inproceedings{paparrizos_sigmod15,
title = {{k-Shape: Efficient and Accurate Clustering of Time Series}},
author = {Paparrizos, John and Gravano, Luis},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2723779},
url = {https://dl.acm.org/doi/10.1145/2723372.2723779},
year = {2015}
}
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