Ottimizzazione del clustering K-Means per i set di dati meteorologici
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Ma questi algoritmi di clustering non sono supervisionati, per cui a volte possono verificarsi impurità a causa di partizioni disuguali. Le impurità possono riguardare la dimensione dei cluster, l'intervallo dei valori dei dati, la deviazione standard dei valori raggruppati, ecc.
Per questo motivo è necessario un meccanismo di controllo supervisionato per ottenere risultati efficaci e accurati. Il lavoro presentato va nella stessa direzione di raggiungere l'efficacia implementando la supervisione dinamica.
Nel lavoro, abbiamo implementato un meccanismo di controllo prima e dopo il processo di clustering. .
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£27.18
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£10.58
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