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Utility Pattern Mining using Python

Om Utility Pattern Mining using Python

Data mining is the process of discovering useful patterns, correlations, and insights from large datasets using computational and statistical techniques. It involves extracting knowledge from data by identifying patterns, trends, and relationships that are not immediately apparent. The goal of data mining is to use data to make informed decisions or predictions. Data mining uses various techniques such as clustering, classification, association rule mining, regression analysis, and outlier detection. These techniques can be applied to structured data, such as relational databases, as well as unstructured data, such as text, images, and videos. The Book, ¿Utility Pattern Mining using Python¿ is simple and easy to learn. This Book is useful for students and professionals of Computer Science and Engineering, Software, Information Technology and Data Science.

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  • Språk:
  • Engelska
  • ISBN:
  • 9786205522714
  • Format:
  • Häftad
  • Sidor:
  • 96
  • Utgiven:
  • 15. maj 2023
  • Mått:
  • 150x6x220 mm.
  • Vikt:
  • 161 g.
  Fri leverans
Leveranstid: 2-4 veckor
Förväntad leverans: 23. december 2024
Förlängd ångerrätt till 31. januari 2025

Beskrivning av Utility Pattern Mining using Python

Data mining is the process of discovering useful patterns, correlations, and insights from large datasets using computational and statistical techniques. It involves extracting knowledge from data by identifying patterns, trends, and relationships that are not immediately apparent. The goal of data mining is to use data to make informed decisions or predictions. Data mining uses various techniques such as clustering, classification, association rule mining, regression analysis, and outlier detection. These techniques can be applied to structured data, such as relational databases, as well as unstructured data, such as text, images, and videos. The Book, ¿Utility Pattern Mining using Python¿ is simple and easy to learn. This Book is useful for students and professionals of Computer Science and Engineering, Software, Information Technology and Data Science.

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