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  3. Data Mining and Data Warehousing
  • Cited by 24
Publisher:
Cambridge University Press
Online publication date:
April 2019
Print publication year:
2019
Online ISBN:
9781108635592
DOI:
https://doi.org/10.1017/9781108635592
Subjects:
Knowledge Management, Databases and Data Mining, Computer Science, Pattern Recognition and Machine Learning
Information

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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