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