The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Clustering by fast search and find of density peaks
Open accessAbstract
Discerning clusters of data points: Cluster analysis is used in many disciplines to group objects according to a defined measure of distance. Numerous algorithms exist, some based on the analysis of the local density of data points, and others on predefined probability distributions. Rodriguez and Laio devised a method in which the cluster centers are recognized as local density maxima that are far away from any points of higher density. The algorithm depends only on the relative densities rather than their absolute values. The authors tested the method on a series of data sets, and its performance compared favorably to that of established techniques. Science, this issue p.1492
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
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 20,598
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,474
Data clustering: 50 years beyond K-means
Jain, A. K. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.09.011 · Cited by 9,368
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
Clustering by Passing Messages Between Data Points
Frey, B. J., & Dueck, D. (2007) · Science · doi:10.1126/science.1136800 · Cited by 6,758
Survey of Clustering Algorithms
Xu, R., & WunschII, D. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.845141 · Cited by 6,197
The EM Algorithm and Extensions
Kushary, D. (1998) · Technometrics · doi:10.1080/00401706.1998.10485534 · Cited by 5,417
Mean shift, mode seeking, and clustering
Cheng, Y. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.400568 · Cited by 3,856
The estimation of the gradient of a density function, with applications in pattern recognition
Fukunaga, K., & Hostetler, L. (1975) · IEEE Transactions on Information Theory · doi:10.1109/tit.1975.1055330 · Cited by 3,043
The EM Algorithm and Extensions, 2E
McLachlan, G. J., & Krishnan, T. (2008) · Wiley Series in Probability and Statistics · doi:10.1002/9780470191613 · Cited by 1,529
Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition
Rayward-Smith, V. J., Höppner, F., Klawonn, F., Kruse, R., & Runkler, T. A. (2000) · Journal of the Operational Research Society · doi:10.2307/254022 · Cited by 922
Gionis, A., Mannila, H., & Tsaparas, P. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217303 · Cited by 819
Complex Wavelet Structural Similarity: A New Image Similarity Index
Sampat, M. P., Wang, Z., Gupta, S., Bovik, A. C., & Markey, M. K. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2025923 · Cited by 656
Robust path-based spectral clustering
Chang, H., & Yeung, D.-Y. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2007.04.010 · Cited by 565
FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data
Fu, L., & Medico, E. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-3 · Cited by 527
Iterative shrinking method for clustering problems
Fränti, P., & Virmajoki, O. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2005.09.012 · Cited by 341
Fast Agglomerative Clustering Using a k-Nearest Neighbor Graph
Fränti, P., Virmajoki, O., & Hautamäki, V. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.227 · Cited by 310
Non-metric affinity propagation for unsupervised image categorization
Dueck, D., & Frey, B. J. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408853 · Cited by 297
A Kinetic Model of Trp-Cage Folding from Multiple Biased Molecular Dynamics Simulations
Marinelli, F., Pietrucci, F., Laio, A., & Piana, S. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000452 · Cited by 274
Self-citation
Cited by
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070
Ziegler, C. G. K., Allon, S. J., Nyquist, S. K., Mbano, I. M., Miao, V. N., Tzouanas, C. N., Cao, Y., Yousif, A. S., Bals, J., Hauser, B. M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.035 · Cited by 2,284
A Comprehensive Survey of Clustering Algorithms
Xu, D., & Tian, Y. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0040-1 · Cited by 1,939
A single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750
Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing
Guo, X., Zhang, Y., Zheng, L., Zheng, C., Song, J., Zhang, Q., Kang, B., Liu, Z., Jin, L., Xing, R., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0045-3 · Cited by 1,651
Metadata
- Authors
- Rodriguez, A., & Laio, A.
- Venue
- Science
- Published
- 2014-06-27
- Volume
- 344
- Issue
- 6191
- Pages
- 1492-1496
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2165835468
- Cited by
- 4,942
- References
- 19
- FWCI
- 240.65
- Type
- journal article
- Language
- en
- Open access
- green
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
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.
