The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
A review of moving object trajectory clustering algorithms
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
(1996) · Cited by 20,179
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724 · cited 2×
“Wang et al. (2013) and Yan et al. (2012) develop a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations on basis of Semantic Model and a Computation and Annotation Platform.”
BackgroundMethodR-trees: a dynamic index structure for spatial searching
Guttman, A. (1984) · Proceedings of the 1984 ACM SIGMOD international conference on Management of data - SIGMOD '84 · doi:10.1145/602259.602266 · Cited by 6,580
Model-Based Clustering, Discriminant Analysis, and Density Estimation
Fraley, C., & Raftery, A. E. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502760047131 · Cited by 4,257
The R*-tree: an efficient and robust access method for points and rectangles
Beckmann, N., Kriegel, H.-P., Schneider, R., & Seeger, B. (1990) · Proceedings of the 1990 ACM SIGMOD international conference on Management of data · doi:10.1145/93597.98741 · Cited by 4,161
Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
BIRCH: an efficient data clustering method for very large databases
Zhang, T., Ramakrishnan, R., & Livny, M. (1996) · Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96 · doi:10.1145/233269.233324 · Cited by 3,935
On Clustering Validation Techniques
Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001) · Journal of Intelligent Information Systems · doi:10.1023/a:1012801612483 · Cited by 2,646
Clustering of time series data—a survey
Warren Liao, T. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.01.025 · Cited by 2,401
Metadata
- Authors
- Yuan, G., Sun, P., Zhao, J., Li, D., & Wang, C.
- Venue
- Artificial Intelligence Review
- Published
- 2016-03-25
- Volume
- 47
- Issue
- 1
- Pages
- 123-144
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2322990480
- Cited by
- 323
- References
- 51
- FWCI
- 29.29
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A review of moving object trajectory clustering algorithms. https://app.cochat.ai/papers/10.1007/s10462-016-9477-7
- The paper
- Yuan, G., Sun, P., Zhao, J., Li, D., & Wang, C. (2017). A review of moving object trajectory clustering algorithms. Artificial Intelligence Review. https://doi.org/10.1007/s10462-016-9477-7
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
