APPROCCIO DI DATA MINING BASATO SULLO STREAM MINING: UN APPROCCIO BASATO SULLO STREAM MINING PER UN AMBIENTE DINAMICO UTILIZZANDO L'ALGORITMO K-MEANS++ (Italian Edition)
Language: Italian
Published by Edizioni Sapienza, 2024
- Softcover
- New

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- Title
- APPROCCIO DI DATA MINING BASATO SULLO STREAM MINING: UN APPROCCIO BASATO SULLO STREAM MINING PER UN AMBIENTE DINAMICO UTILIZZANDO L'ALGORITMO K-MEANS++ (Italian Edition)
- Author
- S, SHYLAJA
- Publisher
- Edizioni Sapienza
- Publication year
- 2024
- Condition
- New
- Binding
- Soft cover
- Language
- Italian
- ISBN 10
- 6207272315
- ISBN 13
- 9786207272310
Il clustering è una delle tecniche più importanti del data mining. Ha lo scopo di suddividere i dati in gruppi di oggetti simili. Questo viene definito cluster. Questa ricerca confronta l'algoritmo StreamKM++ con i lavori esistenti, come AP, IAPKM e IAPNA. L'algoritmo StreamKM++ è un nuovo algoritmo di clustering del flusso di dati e costruisce un buon clustering del flusso, utilizzando una piccola quantità di memoria e di tempo.Molti ricercatori hanno svolto il loro lavoro con un algoritmo di clustering statico, ma in tempo reale i dati sono di natura dinamica. Come i blog, le pagine web, gli audio e i video, ecc... Per questo motivo, la tecnica statica convenzionale non supporta l'ambiente in tempo reale. In questo lavoro, viene utilizzato l'algoritmo StreamKM++ che raggiunge elevate prestazioni di clustering rispetto ai tradizionali AP, IAPKM e IAPNA. I risultati sperimentali mostrano che l'algoritmo StreamKM++ ottiene il miglior risultato rispetto ai lavori esistenti. Ha aumentato il tasso di accuratezza medio e ha ridotto il tempo di calcolo, la memoria e il numero di iterazioni.
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Ria Christie Collections
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5-star seller
AbeBooks seller since March 25, 2015
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