Clustering by passing messages between data points. Export

@article{citeulike:1051630, abstract = {Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called "affinity propagation," which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.}, address = {Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada.}, author = {Frey, Brendan J. and Dueck, Delbert}, citeulike-article-id = {1051630}, citeulike-linkout-0 = {http://dx.doi.org/10.1126/science.1136800}, citeulike-linkout-1 = {http://www.sciencemag.org/cgi/content/abstract/315/5814/972}, citeulike-linkout-2 = {http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2007Sci...315..972F}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/17218491}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=17218491}, day = {16}, doi = {10.1126/science.1136800}, issn = {1095-9203}, journal = {Science (New York, N.Y.)}, keywords = {595w}, month = {February}, number = {5814}, pages = {972--976}, posted-at = {2009-11-10 20:49:24}, priority = {2}, title = {Clustering by passing messages between data points.}, url = {http://dx.doi.org/10.1126/science.1136800}, volume = {315}, year = {2007} }

TY - JOUR ID - citeulike:1051630 L3 - citeulike-article-id:1051630 N2 - Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called "affinity propagation," which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time. IS - 5814 JF - Science (New York, N.Y.) SN - 1095-9203 AD - Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada. EP - 976 TI - Clustering by passing messages between data points. VL - 315 SP - 972 KW - 595w AU - Frey, Brendan AU - Dueck, Delbert PY - 2007/02/16/ UR - http://dx.doi.org/10.1126/science.1136800 ER -