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Analysis with Unsupervised Learning Based Techniques of Load Factor Profiles and Hyperspectral Images

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dc.contributor.author PENTIUC, Ştefan-Gheorghe
dc.contributor.author BOBRIC, Elena Crenguța
dc.contributor.author BILIUS, Laura-Bianca
dc.date.accessioned 2022-12-27T16:43:54Z
dc.date.available 2022-12-27T16:43:54Z
dc.date.issued 2022
dc.identifier.citation PENTIUC, Ştefan-Gheorghe, BOBRIC, Elena Crenguța, BILIUS, Laura-Bianca. Analysis with Unsupervised Learning Based Techniques of Load Factor Profiles and Hyperspectral Images. In: Electronics, Communications and Computing (IC ECCO-2022): 12th intern. conf., 20-21 Oct. 2022, Chişinău, Republica Moldova: conf. proc., Chişinău, 2022, pp. 136-139. en_US
dc.identifier.uri https://doi.org/10.52326/ic-ecco.2022/SEC.05
dc.identifier.uri http://repository.utm.md/handle/5014/21844
dc.description.abstract The problem of obtaining an optimal partition consistent with a series of partitions resulting from the application of various clustering algorithms is NP complete. A heuristic method based on the concepts of central partition and strong patterns developed by Edwin Diday [3] is proposed. It is presented the experience regarding the use of analysis techniques based on unsupervised learning methods of load factor profiles and hyperspectral images. en_US
dc.language.iso en en_US
dc.publisher Technical University of Moldova en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject machine learning en_US
dc.subject unsupervised learning en_US
dc.subject clustering algorithms en_US
dc.subject load factor profiles en_US
dc.subject hyperspectral images en_US
dc.title Analysis with Unsupervised Learning Based Techniques of Load Factor Profiles and Hyperspectral Images en_US
dc.type Article en_US


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  • 2022
    Proceedings of the 12th IC|ECCO; October 20-21, 2022

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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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