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| Ultima descărcare din IBN: 2023-06-14 07:34 |
 SM ISO690:2012PENTIUC, Stefan-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, Ed. 12, 20-21 octombrie 2022, Chişinău. Chișinău: Tehnica-UTM, 2023, Editia 12, pp. 136-139. DOI: https://doi.org/10.52326/ic-ecco.2022/SEC.05 |
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| Electronics, Communications and Computing Editia 12, 2023 |
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Conferința "Electronics, Communications and Computing" 12, Chişinău, Moldova, 20-21 octombrie 2022 | ||||||
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| DOI:https://doi.org/10.52326/ic-ecco.2022/SEC.05 | ||||||
| Pag. 136-139 | ||||||
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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. |
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| Cuvinte-cheie machine learning, unsupervised learning, Clustering algorithms, load factor profiles, hyperspectral images |
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Dublin Core Export
<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc='http://purl.org/dc/elements/1.1/' xmlns:oai_dc='http://www.openarchives.org/OAI/2.0/oai_dc/' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xsi:schemaLocation='http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd'> <dc:creator>Pentiuc, S.</dc:creator> <dc:creator>Bobric, E.</dc:creator> <dc:creator>Bilius, L.</dc:creator> <dc:date>2023</dc:date> <dc:description xml:lang='en'><p>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.</p></dc:description> <dc:source>Electronics, Communications and Computing (Editia 12) 136-139</dc:source> <dc:subject>machine learning</dc:subject> <dc:subject>unsupervised learning</dc:subject> <dc:subject>Clustering algorithms</dc:subject> <dc:subject>load factor profiles</dc:subject> <dc:subject>hyperspectral images</dc:subject> <dc:title>Analysis with Unsupervised Learning Based Techniques of Load Factor Profiles and Hyperspectral Images</dc:title> <dc:type>info:eu-repo/semantics/article</dc:type> </oai_dc:dc>
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