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
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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
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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
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