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  3. Data Mining and Data Warehousing
  • Cited by 26
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    • Publisher:
      Cambridge University Press
      Publication date:
      April 2019
      May 2019
      ISBN:
      9781108635592
      9781108727747
      DOI:
      https://doi.org/10.1017/9781108635592
      Dimensions:
      Weight & Pages:
      Dimensions:
      (244 x 170 mm)
      Weight & Pages:
      0.66kg, 506 Pages
      • Subjects:
      Knowledge Management, Databases and Data Mining, Computer Science, Pattern Recognition and Machine Learning
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    Subjects:
    Knowledge Management, Databases and Data Mining, Computer Science, Pattern Recognition and Machine Learning
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    Book description

    Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding.

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