K -MLIO: Enabling K -Means for Large Data-Sets and Memory Constrained Embedded Systems

Camelia Slimani; Stéphane Rubini; Jalil Boukhobza

doi:10.1109/MASCOTS.2019.00037

Conference Papers Year : 2019

K -MLIO: Enabling K -Means for Large Data-Sets and Memory Constrained Embedded Systems

(1) , (2) , (2)

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Camelia Slimani

Function : Author

Lab-STICC_UBO_CACS_MOCS

Stéphane Rubini

Function : Author
PersonId : 13231
IdHAL : stephane-rubini
ORCID : 0000-0002-3206-0310
IdRef : 223329940

Lab-STICC_UBO_CACS_MOCS

Jalil Boukhobza

Function : Author
PersonId : 1840
IdHAL : jalil-boukhobza
ORCID : 0000-0002-2194-4006
IdRef : 09772582X

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Operating Systems [cs.OS] Embedded Systems Artificial Intelligence [cs.AI]

Jalil Boukhobza : Connect in order to contact the contributor

https://hal.univ-brest.fr/hal-02482026

Submitted on : Monday, February 17, 2020-5:57:08 PM

Last modification on : Friday, March 24, 2023-2:53:15 PM

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hal-02482026 , version 1 (17-02-2020)

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HAL Id : hal-02482026 , version 1
DOI : 10.1109/MASCOTS.2019.00037

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Camelia Slimani, Stéphane Rubini, Jalil Boukhobza. K -MLIO: Enabling K -Means for Large Data-Sets and Memory Constrained Embedded Systems. 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Oct 2019, Rennes, France. pp.262-268, ⟨10.1109/MASCOTS.2019.00037⟩. ⟨hal-02482026⟩

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K -MLIO: Enabling K -Means for Large Data-Sets and Memory Constrained Embedded Systems

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