000			02562nam a22002057a 4500
999			_c2488 _d2488
003			OSt
005			20200224112009.0
008			200118b ||||| |||| 00| 0 eng d
020			_a978-1-108-72774-7
028			_bAllied Informatics, Jaipur _c7084 _d13/01/2020 _q2019-20
040			_aBSDU _bEnglish _cBSDU
082			_a006.312 _bBHA
100			_aBhatia,Parteek
245			_aData Mining and Data Warehousing:Principles and Practical Techniques
260			_aUnited Kingdom _bCambridge University Press _c2019
300			_a477
504			_aWritten 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. Discusses important concepts with their practical implementation using Weka and R language data mining tools Includes advanced topics such as big data analytics, relational data models and NoSQL that are discussed in detail Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding Preface Acknowledgement Dedication 1. Beginning with machine learning 2. Introduction to data mining 3. Beginning with Weka and R language 4. Data pre-processing 5. Classification 6. Implementing classification in Weka and R 7. Cluster analysis 8. Implementing clustering with Weka and R 9. Association mining 10. Implementing association mining with Weka and R 11. Web mining and search engine 12. Operational data store and data warehouse 13. Data warehouse schema 14. Online analytical processing 15. Big data and NoSQL
650			_aData Mining
942			_2ddc _cBK