approccio di apprendimento supervisionato per l'identificazione dei criminali
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approccio di apprendimento supervisionato per l'identificazione dei criminali

approccio di apprendimento supervisionato per l'identificazione dei criminali


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About the Book

Gli strumenti di data mining sono l'approccio migliore per l'identificazione dei criminali in base alle caratteristiche e alla natura del reato. In questo libro, abbiamo proposto un approccio supervisionato per l'identificazione di liste sospette di criminali utilizzando misure di similarità e l'algoritmo di clustering K-Medoids. L'algoritmo di clustering K-Medoids raggruppa i crimini più strettamente correlati come un gruppo individuale e ogni gruppo avrà un insieme unico di caratteristiche. L'insieme di caratteristiche uniche viene utilizzato per l'identificazione dei criminali utilizzando algoritmi di misura della somiglianza basati sulla misura della distanza. Il sistema proposto prevede due fasi, quella di addestramento e quella di test. In questo approccio, abbiamo addestrato il sistema proposto con un set di dati supervisionati con informazioni sulla criminalità raccolte da vari luoghi del Tamil Nadu attraverso i dati disponibili online. Nella fase di test, prima si identifica il cluster più vicino al crimine in esame utilizzando l'algoritmo di clustering K-Medoids e poi si identifica l'elenco dei sospetti criminali utilizzando la misura di similarità. La fase iniziale di implementazione e analisi dello schema proposto fornisce buoni risultati e un'elevata accuratezza. Lo schema proposto viene confrontato con il relativo algoritmo di clustering K-Means con lo stesso set di istanze di addestramento e di test.


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Product Details
  • ISBN-13: 9786205748794
  • Publisher: KS Omniscriptum Publishing
  • Binding: Paperback
  • Language: Italian
  • Returnable: N
  • Weight: 145 gr
  • ISBN-10: 6205748797
  • Publisher Date: 03 Jan 2024
  • Height: 229 mm
  • No of Pages: 56
  • Spine Width: 3 mm
  • Width: 152 mm


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