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

Pentiuc Stefan-Gheorghe; Bobric Elena Crenguța; Bilius Laura-Bianca

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Ultima descărcare din IBN:
2023-06-14 07:34

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PENTIUC, 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

Conferința "Electronics, Communications and Computing"
12, Chişinău, Moldova, 20-21 octombrie 2022

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

DOI:https://doi.org/10.52326/ic-ecco.2022/SEC.05

Pag. 136-139

Pentiuc Stefan-Gheorghe, Bobric Elena Crenguța, Bilius Laura-Bianca

„Ștefan cel Mare” University, Suceava

Disponibil în IBN: 3 aprilie 2023

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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.

Cuvinte-cheie
machine learning, unsupervised learning, Clustering algorithms, load factor profiles, hyperspectral images

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