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Gribel, Daniel, Vidal, Thibaut (2019) HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering. Pattern Recognition, 88. 569-583 doi:10.1016/j.patcog.2018.12.022

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Reference TypeJournal (article/letter/editorial)
TitleHG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering
JournalPattern Recognition
AuthorsGribel, DanielAuthor
Vidal, ThibautAuthor
Year2019 (April)Volume88
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2018.12.022Search in ResearchGate
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Mindat Ref. ID9367690Long-form Identifiermindat:1:5:9367690:6
GUID0
Full ReferenceGribel, Daniel, Vidal, Thibaut (2019) HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering. Pattern Recognition, 88. 569-583 doi:10.1016/j.patcog.2018.12.022
Plain TextGribel, Daniel, Vidal, Thibaut (2019) HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering. Pattern Recognition, 88. 569-583 doi:10.1016/j.patcog.2018.12.022
In(2019) Pattern Recognition Vol. 88. Elsevier BV

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Bagirov, Adil M. (2008) Modified global -means algorithm for minimum sum-of-squares clustering problems. Pattern Recognition, 41 (10). 3192-3199 doi:10.1016/j.patcog.2008.04.004
Hansen, Pierre, Mladenović, Nenad (2001) J-Means: a new local search heuristic for minimum sum of squares clustering. Pattern Recognition, 34 (2). 405-413 doi:10.1016/s0031-3203(99)00216-2
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