Data mining and data warehousing : principles and practical techniques /
Bhatia, Parteek,
Data mining and data warehousing : principles and practical techniques / Parteek Bhatia - xxiv, 477 pages 24 cm.
Includes bibliographical references and index
Beginning with machine learning -- Introduction to data mining -- Beginning with Weka and R language -- Data preprocessing -- Classification -- Implementing classification in Weka and R -- Cluster analysis -- Implementing clustering with Weka and R -- Association mining -- Implementing association mining with Weka and R -- Web mining and search engines -- Data warehouse -- Data warehouse schema -- Online analytical processing -- Big data and NoSQL
This 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. It brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Na©¯ve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. 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. Unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding--
9781108727747;1108727743
2019013358
Data mining
Data warehousing
QA 76.9.D343 / .B469 2019
Data mining and data warehousing : principles and practical techniques / Parteek Bhatia - xxiv, 477 pages 24 cm.
Includes bibliographical references and index
Beginning with machine learning -- Introduction to data mining -- Beginning with Weka and R language -- Data preprocessing -- Classification -- Implementing classification in Weka and R -- Cluster analysis -- Implementing clustering with Weka and R -- Association mining -- Implementing association mining with Weka and R -- Web mining and search engines -- Data warehouse -- Data warehouse schema -- Online analytical processing -- Big data and NoSQL
This 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. It brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Na©¯ve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. 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. Unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding--
9781108727747;1108727743
2019013358
Data mining
Data warehousing
QA 76.9.D343 / .B469 2019
