Data mining and data warehousing : Principles and practical techniques
By: Bhatia, Parteek
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Publisher: New Delhi Cambridge University Press 2019Description: xxxiv, 477p. | Binding - Paperback | 24*18 cm.ISBN: 9781108727747.Subject(s): Computer EngineeringDDC classification: 006.312 Summary: 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.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Text Books
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School of Engineering & Technology Reference Section | Reference | 006.312 BHA (Browse shelf) | Available | E15168 |
Browsing School of Engineering & Technology Shelves , Shelving location: Reference Section , Collection code: Reference Close shelf browser
| 006.31 PAL/THO MATLAB machine learning recipes | 006.31 ZHE/CAS Feature engineering for machine learning | 006.312 ACH Data analytics using R | 006.312 BHA Data mining and data warehousing | 006.312 CHE Pandas for everyone | 006.312 HAN Practical data mining | 006.312 HAN/KAM Data mining concepts and techniques |
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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